From d76f3198bb173bf8ebd9da7228d1bb09b57eb1e2 Mon Sep 17 00:00:00 2001 From: Rashid N H M <95639609+rashidnhm@users.noreply.github.com> Date: Wed, 16 Aug 2023 09:51:18 -0400 Subject: [PATCH 01/24] Added upload per env, and added new staging env (#891) * Updated JSON building workflow into one upload job per environment * Update .github/workflows/build-json.yml Co-authored-by: Mikhail Andrenkov * Move aws secrets in upload job to be global at job level --------- Co-authored-by: Mikhail Andrenkov --- .github/workflows/build-json.yml | 138 ++++++++++-------------------- .github/workflows/upload-json.yml | 125 +++++++++++++++++++++++++++ 2 files changed, 172 insertions(+), 91 deletions(-) create mode 100644 .github/workflows/upload-json.yml diff --git a/.github/workflows/build-json.yml b/.github/workflows/build-json.yml index 693d5ca5ca..b292e1ad4a 100644 --- a/.github/workflows/build-json.yml +++ b/.github/workflows/build-json.yml @@ -22,103 +22,59 @@ jobs: skip_sphinx_build_file_aggregation: false sphinx_build_output_format: json - upload: - runs-on: ubuntu-latest - + upload-production: + if: github.ref_name == 'master' needs: - build + uses: ./.github/workflows/upload-json.yml + with: + branch: ${{ github.ref }} + aws_s3_bucket_dir: commits/${{ github.ref_name }}-${{ github.sha }} + artifact_name: json.zip + qml_react_webhook_event_type: build-pl-site-main - steps: - - name: Checkout - uses: actions/checkout@v3 - - - name: Download HTML - id: qml_json - uses: actions/download-artifact@v3 - with: - name: json.zip - path: qml_json - - - name: Copy Metadata into Build - run: | - mkdir ${{ steps.qml_json.outputs.download-path }}/metadata - find demonstrations -name "*.metadata.json" -type f | xargs cp -vt ${{ steps.qml_json.outputs.download-path }}/metadata - - - name: Generate searchindex.js file - run: | - touch searchindex.js - echo -n 'Search.setIndex(' > searchindex.js - cat ${{ steps.qml_json.outputs.download-path }}/searchindex.json >> searchindex.js - echo -n ')' >> searchindex.js - - - name: Set Upload Info - id: upload_info - env: - INFRA_ENV: ${{ github.ref_name == 'master' && 'PROD' || 'DEV' }} - run: | - AWS_REGION=${{ secrets.AWS_REGION }} - AWS_ACCESS_KEY_ID=${{ secrets[format('PL_SITE_{0}_NON_REACT_ACCESS_KEY_ID', env.INFRA_ENV)] }} - AWS_SECRET_ACCESS_KEY=${{ secrets[format('PL_SITE_{0}_NON_REACT_SECRET_ACCESS_KEY', env.INFRA_ENV)] }} - AWS_S3_BUCKET_ID=${{ secrets[format('PL_SITE_{0}_QML_JSON_S3_BUCKET_ID', env.INFRA_ENV)] }} - AWS_S3_BUCKET_DIR='commits/${{ github.ref_name }}-${{ github.sha }}' - AWS_HTML_S3_BUCKET_ID=${{ secrets[format('PL_SITE_{0}_S3_BUCKET_NAME', env.INFRA_ENV)] }} - - echo "aws_region=$AWS_REGION" >> $GITHUB_OUTPUT - echo "aws_access_key_id=$AWS_ACCESS_KEY_ID" >> $GITHUB_OUTPUT - echo "aws_secret_access_key=$AWS_SECRET_ACCESS_KEY" >> $GITHUB_OUTPUT - echo "aws_s3_bucket_id=$AWS_S3_BUCKET_ID" >> $GITHUB_OUTPUT - echo "aws_s3_bucket_dir=$AWS_S3_BUCKET_DIR" >> $GITHUB_OUTPUT - echo "aws_html_s3_bucket_id=$AWS_HTML_S3_BUCKET_ID" >> $GITHUB_OUTPUT - - - name: Upload - uses: XanaduAI/cloud-actions/push-to-s3-and-invalidate-cloudfront@main - with: - build-directory: ${{ steps.qml_json.outputs.download-path }} - aws-cloudfront-distribution-id: '' - aws-region: ${{ steps.upload_info.outputs.aws_region }} - aws-access-key-id: ${{ steps.upload_info.outputs.aws_access_key_id }} - aws-secret-access-key: ${{ steps.upload_info.outputs.aws_secret_access_key }} - s3-bucket: ${{ steps.upload_info.outputs.aws_s3_bucket_id }} - s3-directory: ${{ steps.upload_info.outputs.aws_s3_bucket_dir }} - s3-delete-stale-files: true - s3-action: upload - invalidate-cloudfront-cache: false - - - name: Set as latest upload - env: - AWS_REGION: ${{ steps.upload_info.outputs.aws_region }} - AWS_ACCESS_KEY_ID: ${{ steps.upload_info.outputs.aws_access_key_id }} - AWS_SECRET_ACCESS_KEY: ${{ steps.upload_info.outputs.aws_secret_access_key }} - AWS_S3_BUCKET_ID: ${{ steps.upload_info.outputs.aws_s3_bucket_id }} - AWS_S3_BUCKET_DIR: ${{ steps.upload_info.outputs.aws_s3_bucket_dir }} - run: aws s3 sync s3://$AWS_S3_BUCKET_ID/$AWS_S3_BUCKET_DIR s3://$AWS_S3_BUCKET_ID/${{ github.ref_name }} --delete + secrets: + aws_region: ${{ secrets.AWS_REGION }} + aws_access_key_id: ${{ secrets.PL_SITE_PROD_NON_REACT_ACCESS_KEY_ID }} + aws_secret_access_key: ${{ secrets.PL_SITE_PROD_NON_REACT_SECRET_ACCESS_KEY }} + aws_json_s3_bucket_id: ${{ secrets.PL_SITE_PROD_QML_JSON_S3_BUCKET_ID }} + aws_html_s3_bucket_id: ${{ secrets.PL_SITE_PROD_S3_BUCKET_NAME }} + qml_react_pat_token: ${{ secrets.PL_SITE_PROD_QML_REACT_PAT }} - - name: Sync searchindex to HTML Bucket - env: - AWS_REGION: ${{ steps.upload_info.outputs.aws_region }} - AWS_ACCESS_KEY_ID: ${{ steps.upload_info.outputs.aws_access_key_id }} - AWS_SECRET_ACCESS_KEY: ${{ steps.upload_info.outputs.aws_secret_access_key }} - AWS_S3_BUCKET_ID: ${{ steps.upload_info.outputs.aws_html_s3_bucket_id }} - run: aws s3 cp searchindex.js s3://$AWS_S3_BUCKET_ID/qml/searchindex.js + upload-staging: + if: github.ref_name == 'dev' + needs: + - build + uses: ./.github/workflows/upload-json.yml + with: + branch: ${{ github.ref }} + aws_s3_bucket_dir: commits/${{ github.ref_name }}-${{ github.sha }} + artifact_name: json.zip + qml_react_webhook_event_type: build-pl-site-staging - # Though this job only has one step to trigger a build in the pennylane.ai React repository, it has to be a separate - # job since we need the previous two to be successful before this can be done. - trigger-website-build: - runs-on: ubuntu-latest + secrets: + aws_region: ${{ secrets.AWS_REGION }} + aws_access_key_id: ${{ secrets.PL_SITE_STAGING_NON_REACT_ACCESS_KEY_ID }} + aws_secret_access_key: ${{ secrets.PL_SITE_STAGING_NON_REACT_SECRET_ACCESS_KEY }} + aws_json_s3_bucket_id: ${{ secrets.PL_SITE_STAGING_QML_JSON_S3_BUCKET_ID }} + aws_html_s3_bucket_id: ${{ secrets.PL_SITE_STAGING_S3_BUCKET_NAME }} + qml_react_pat_token: ${{ secrets.PL_SITE_STAGING_QML_REACT_PAT }} + upload-dev: + if: github.ref_name == 'dev' needs: - build - - upload + uses: ./.github/workflows/upload-json.yml + with: + branch: ${{ github.ref }} + aws_s3_bucket_dir: commits/${{ github.ref_name }}-${{ github.sha }} + artifact_name: json.zip + qml_react_webhook_event_type: build-pl-site-dev - steps: - - name: Trigger Website Build - uses: peter-evans/repository-dispatch@v2 - env: - INFRA_ENV: ${{ github.ref_name == 'master' && 'PROD' || 'DEV' }} - EVENT_NAME: build-pl-site-${{ github.ref_name == 'master' && 'main' || 'dev' }} - RUN_URL: ${{ github.server_url }}/${{ github.repository }}/actions/runs/${{ github.run_id }} - with: - token: ${{ secrets[format('PL_SITE_{0}_QML_REACT_PAT', env.INFRA_ENV)] }} - repository: XanaduAI/pennylane.ai-react - event-type: ${{ env.EVENT_NAME }} - client-payload: '{"source_ref": "${{ github.ref_name }}", "source_sha": "${{ github.sha }}", "source_run_url": "${{ env.RUN_URL }}"}' + secrets: + aws_region: ${{ secrets.AWS_REGION }} + aws_access_key_id: ${{ secrets.PL_SITE_DEV_NON_REACT_ACCESS_KEY_ID }} + aws_secret_access_key: ${{ secrets.PL_SITE_DEV_NON_REACT_SECRET_ACCESS_KEY }} + aws_json_s3_bucket_id: ${{ secrets.PL_SITE_DEV_QML_JSON_S3_BUCKET_ID }} + aws_html_s3_bucket_id: ${{ secrets.PL_SITE_DEV_S3_BUCKET_NAME }} + qml_react_pat_token: ${{ secrets.PL_SITE_DEV_QML_REACT_PAT }} diff --git a/.github/workflows/upload-json.yml b/.github/workflows/upload-json.yml new file mode 100644 index 0000000000..5d95266fd2 --- /dev/null +++ b/.github/workflows/upload-json.yml @@ -0,0 +1,125 @@ +name: Upload JSON to S3 +on: + workflow_call: + inputs: + branch: + description: The QML branch that is being built + type: string + required: true + aws_s3_bucket_dir: + description: The directory within the S3 bucket where the files will be pushed to + type: string + required: true + artifact_name: + description: The name of the artifact which contains all the JSON files to be downloaded + type: string + required: false + default: json.zip + qml_react_webhook_event_type: + description: The event-type of the webhook to trigger a build on the website once the files are uploaded + type: string + required: true + + secrets: + aws_region: + description: The AWS Region the infrastructure resides in + required: true + aws_access_key_id: + description: AWS Access Key to use when accessing the S3 bucket + required: true + aws_secret_access_key: + description: AWS Secret Access Key to use when accessing the S3 bucket + required: true + aws_json_s3_bucket_id: + description: The S3 bucket ID where the JSON files will be pushed + required: true + aws_html_s3_bucket_id: + description: The S3 bucket ID where the searchindex.js file will be pushed + required: true + qml_react_pat_token: + description: The token that will be used to trigger a build on the react website + required: true + +jobs: + upload: + runs-on: ubuntu-latest + + env: + AWS_REGION: ${{ secrets.aws_region }} + AWS_ACCESS_KEY_ID: ${{ secrets.aws_access_key_id }} + AWS_SECRET_ACCESS_KEY: ${{ secrets.aws_secret_access_key }} + AWS_S3_BUCKET_ID: ${{ secrets.aws_json_s3_bucket_id }} + AWS_S3_BUCKET_DIR: ${{ inputs.aws_s3_bucket_dir }} + + steps: + - name: Checkout + uses: actions/checkout@v3 + with: + ref: ${{ inputs.branch }} + fetch-depth: 1 + + - name: Download HTML + id: qml_json + uses: actions/download-artifact@v3 + with: + name: ${{ inputs.artifact_name }} + path: qml_json + + - name: Copy Metadata into Build + run: | + mkdir ${{ steps.qml_json.outputs.download-path }}/metadata + find demonstrations -name "*.metadata.json" -type f | xargs cp -vt ${{ steps.qml_json.outputs.download-path }}/metadata + + - name: Generate searchindex.js file + run: | + touch searchindex.js + echo -n 'Search.setIndex(' > searchindex.js + cat ${{ steps.qml_json.outputs.download-path }}/searchindex.json >> searchindex.js + echo -n ')' >> searchindex.js + + - name: Upload + uses: XanaduAI/cloud-actions/push-to-s3-and-invalidate-cloudfront@main + with: + build-directory: ${{ steps.qml_json.outputs.download-path }} + aws-cloudfront-distribution-id: '' + aws-region: ${{ secrets.aws_region }} + aws-access-key-id: ${{ secrets.aws_access_key_id }} + aws-secret-access-key: ${{ secrets.aws_secret_access_key }} + s3-bucket: ${{ secrets.aws_json_s3_bucket_id }} + s3-directory: ${{ inputs.aws_s3_bucket_dir }} + s3-delete-stale-files: true + s3-action: upload + invalidate-cloudfront-cache: false + + - name: Get Branch Name from Ref + id: branch_name + run: | + input_branch="${{ inputs.branch }}" + ref_name=${input_branch#refs/heads/} + echo "name=$ref_name" >> $GITHUB_OUTPUT + + - name: Set as latest upload + run: aws s3 sync s3://$AWS_S3_BUCKET_ID/$AWS_S3_BUCKET_DIR s3://$AWS_S3_BUCKET_ID/${{ steps.branch_name.outputs.name }} --delete + + - name: Sync searchindex to HTML Bucket + run: aws s3 cp searchindex.js s3://$AWS_S3_BUCKET_ID/qml/searchindex.js + + trigger-website-build: + runs-on: ubuntu-latest + + needs: + - upload + + steps: + - name: Trigger Website Build + uses: peter-evans/repository-dispatch@v2 + env: + RUN_URL: ${{ github.server_url }}/${{ github.repository }}/actions/runs/${{ github.run_id }} + with: + token: ${{ secrets.qml_react_pat_token }} + repository: XanaduAI/pennylane.ai-react + event-type: ${{ inputs.qml_react_webhook_event_type }} + client-payload: '{"source_ref": "${{ github.ref_name }}", "source_sha": "${{ github.sha }}", "source_run_url": "${{ env.RUN_URL }}"}' + + + From 651112670f20ffa2050df582207baef0cc41fbf0 Mon Sep 17 00:00:00 2001 From: Guillermo Alonso-Linaje <65235481+KetpuntoG@users.noreply.github.com> Date: Mon, 21 Aug 2023 13:02:47 -0400 Subject: [PATCH 02/24] Adding QSVT manually (#897) * manually added * Update demonstrations/tutorial_apply_qsvt.metadata.json --- _static/authors/jarrett_smalley.png | Bin 0 -> 1666807 bytes _static/authors/jarrett_smalley.txt | 4 + 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"authors": [ + { + "id": "jay_soni" + }, + { + "id": "jarrett_smalley" + } + ], + "dateOfPublication": "2023-08-22T00:00:00+00:00", + "dateOfLastModification": "2023-08-22T00:00:00+00:00", + "categories": [ + "Quantum Computing", + "Algorithms", + "Optimization" + ], + "tags": [], + "previewImages": [ + { + "type": "thumbnail", + "uri": "/_images/thumbnail_tutorial_QSVT_for_Matrix_Inversion.png" + }, + { + "type": "large_thumbnail", + "uri": "/_static/large_demo_thumbnails/thumbnail_large_QSVT_for_Matrix_Inversion.png" + } + ], + "seoDescription": "Applying QSVT for matrix inversion", + "doi": "", + "canonicalURL": "/qml/demos/tutorial_apply_qsvt", + "references": [ + { + "type": "article", + "id": "qsvt", + "title": "Quantum singular value transformation and beyond: exponential improvements for quantum matrix arithmetics", + "authors": "András Gilyén, Yuan Su, Guang Hao Low, and Nathan Wiebe", + "year": "2019", + "publisher": "", + "journal": "", + "doi": "10.1145/3313276.3316366", + "url": "https://dl.acm.org/doi/abs/10.1145/3313276.3316366" + }, + { + "type": "article", + "id": "phaseeval", + "title": "Efficient phase-factor evaluation in quantum signal processing", + "authors": "Dong Y, Meng X, Whaley K and Lin L", + "year": "2021", + "publisher": "", + "journal": "", + "doi": "10.1103/PhysRevA.103.042419", + "url": "https://journals.aps.org/pra/abstract/10.1103/PhysRevA.103.042419" + }, + { + "type": "article", + "id": "machineprecision", + "title": "Finding Angles for Quantum Signal Processing with Machine Precision", + "authors": "Chao R, Ding D, Gilyen A, Huang C, and Szegedy M", + "year": "2020", + "publisher": "", + "journal": "", + "doi": "10.48550/arXiv.2003.02831", + "url": "https://arxiv.org/abs/2003.02831" + }, + { + "type": "article", + "id": "productdecomp", + "title": "Product decomposition of periodic functions in quantum signal processing", + "authors": "Haah J", + "year": "2019", + "publisher": "", + "journal": "Quantum", + "doi": "10.48550/arXiv.2003.02831", + "url": "https://quantum-journal.org/papers/q-2019-10-07-190/" + } + ], + "basedOnPapers": [], + "referencedByPapers": [], + "relatedContent": [ + { + "type": "demonstration", + "id": "tutorial_intro_qsvt", + "weight": 1.0 + }, + { + "type": "demonstration", + "id": "function_fitting_qsp", + "weight": 1.0 + } + ] +} diff --git a/demonstrations/tutorial_apply_qsvt.py b/demonstrations/tutorial_apply_qsvt.py new file mode 100644 index 0000000000..b7c9dde258 --- /dev/null +++ b/demonstrations/tutorial_apply_qsvt.py @@ -0,0 +1,407 @@ +r""" +QSVT in Practice +====================================== + +.. meta:: + :property="og:description": Quantum Singular Value Transformation algorithm + :property="og:image": https://pennylane.ai/qml/_images/thumbnail_intro_qsvt.png + +.. related:: + + tutorial_intro_qsvt Intro to QSVT + function_fitting_qsp Function Fitting using Quantum Signal Processing + +*Authors: Jay Soni, Jarrett Smalley [Rolls-Royce] — Posted: 22 August 2023.* + +The Quantum Singular Value Transformation (QSVT) is a quantum algorithm that +allows us to apply arbitrary polynomial transformations to the singular values +of a matrix [#qsvt]_. This demo, written in collaboration between Xanadu and +Rolls-Royce, provides a practical guide for the QSVT functionality in PennyLane, +by solving a linear system of equations (LSE) as a guiding example. + +| + +.. figure:: ../demonstrations/apply_qsvt/thumbnail_tutorial_QSVT_for_Matrix_Inversion.png + :align: center + :width: 50% + :target: javascript:void(0) + +| + +Preliminaries +------------- +For a refresher on the basics of QSVT check out our :doc:`Intro to QSVT ` +tutorial. Let's recall how to apply QSVT in a circuit. This requires two pieces of information +as input: the matrix to be transformed and a set of phase angles which determine the polynomial +transformation. For now, we use placeholder values for the phase angles; we'll later describe +how to obtain them. The code below shows how to construct a basic QSVT circuit on two qubits: +""" + +import pennylane as qml +from pennylane import numpy as np + +dev = qml.device("default.qubit", wires=[0, 1]) + +A = np.array([[0.1, 0.2], [0.3, 0.4]]) +phase_angles = np.array([0.0, 1.0, 2.0, 3.0]) + + +@qml.qnode(dev) +def my_circuit(phase_angles): + qml.qsvt(A, phase_angles, wires=[0, 1]) + return qml.state() + + +############################################################################### +# We can now execute the circuit and visualize it. + +my_circuit(phase_angles) +print(qml.draw(my_circuit)(phase_angles)) + +############################################################################### +# We can inspect details by drawing the expanded circuit. The :class:`~.pennylane.QSVT` +# operation is composed of repeated applications of the :class:`~.pennylane.BlockEncode` and +# :class:`~.pennylane.PCPhase` (:math:`\Pi_{\phi}`) operations. + +print(my_circuit.tape.expand().draw()) + +############################################################################### +# Now let's look at an application of QSVT --- solving a linear system of equations. +# +# Problem +# ------- +# The most convenient way to represent a linear system of equations is as a matrix vector problem. +# Given a matrix :math:`A` and a vector :math:`\vec{b}`, we want to solve :math:`A \cdot \vec{x} = \vec{b}`. +# This ultimately requires computing :math:`\vec{x} = A^{-1} \cdot \vec{b}`, where for simplicity we +# assume that :math:`A` is invertible. +# +# :math:`A^{-1}` can be constructed directly by inverting the singular values of :math:`A^{T}`. We can +# leverage QSVT to accomplish this by finding the phase angles which apply a polynomial approximation +# to the transformation :math:`\frac{1}{x}`. This may seem simple in theory, but in practice there are +# a few technical details that need to be addressed. +# +# First, it is difficult to approximate :math:`\frac{1}{x}` close to :math:`x=0`. This leads to +# large degree polynomials and very deep quantum circuits. However, it turns out that +# we only need a good approximation up to the smallest singular value of the target matrix. +# We introduce the parameter :math:`\kappa` that defines the domain :math:`[\frac{1}{\kappa}, 1]` for which the +# approximation should be good. +# +# Second, the QSVT algorithm produces polynomials which are bounded in magnitude by one on +# the domain :math:`x \in [-1, 1]`. However, :math:`\frac{1}{x}` falls outside the bounds on +# this domain. To remedy this, we introduce a scale factor :math:`s` and approximate +# :math:`s \cdot \frac{1}{x}`. +# +# Obtaining Phase Angles +# ---------------------- +# The QSVT phase angles :math:`\vec{\phi}` define the polynomial transformation. +# Here we describe two approaches to obtain the phase angles: +# +# 1. Using external packages that provide numerical angle solvers (`pyqsp `_). +# 2. Using PennyLane's differentiable workflow to optimize the phase angles. +# +# Let's use both methods to apply a polynomial transformation that approximates +# +# .. math:: P(x) = s \cdot \frac{1}{x}. +# +# Phase Angles from PyQSP +# ^^^^^^^^^^^^^^^^^^^^^^^ +# There are many methods for computing the phase angles (see [#phaseeval]_, +# [#machineprecision]_, [#productdecomp]_). They can be readily used with +# PennyLane as long as the convention used to define the rotations +# matches the one used when applying QSVT. This is as simple as specifying the +# convention as a keyword argument to the :func:`~.pennylane.qsvt()` function. +# We demonstrate this by obtaining angles using the `pyqsp `_ library. +# +# The phase angles generated from pyqsp are presented below. A value of :math:`\kappa=4` was used +# and the scale factor was extracted from the pyqsp module. Remember that the number of +# phase angles determines the degree of the polynomial approximation. Below we display the 44 +# angles which produce a polynomial of degree 43. + +kappa = 4 +s = 0.10145775 +phi_pyqsp = [-2.287, 2.776, -1.163, 0.408, -0.16, -0.387, 0.385, -0.726, 0.456, 0.062, -0.468, 0.393, 0.028, -0.567, 0.76, -0.432, -0.011, 0.323, -0.573, 0.82, -1.096, 1.407, -1.735, 2.046, -2.321, 2.569, -2.819, -0.011, 2.71, -2.382, 2.574, 0.028, -2.749, 2.673, 0.062, -2.685, 2.416, 0.385, -0.387, -0.16, 0.408, -1.163, -0.365, 2.426] + +############################################################################### +# .. note:: +# +# We generated the angles using the following pyqsp functions. These methods have a randomized +# component which results in slightly different phase angles each time producing the same +# transformation: +# +# .. code-block:: bash +# +# >>> pcoefs, s = pyqsp.poly.PolyOneOverX().generate(kappa, return_coef=True, ensure_bounded=True, return_scale=True) +# >>> phi_pyqsp = pyqsp.angle_sequence.QuantumSignalProcessingPhases(pcoefs, signal_operator="Wx", tolerance=0.00001) +# +# +# Let's confirm that these angles perform the correct transformation. +# We use the :func:`~.pennylane.matrix()` function to obtain the output matrix +# of the QSVT circuit. The top-left entry is a polynomial approximation whose +# real component corresponds to our target function :math:`P(x)`. + +x_vals = np.linspace(0, 1, 50) +target_y_vals = [s * (1 / x) for x in np.linspace(s, 1, 50)] + +qsvt_y_vals = [] +for x in x_vals: + poly_x = qml.matrix(qml.qsvt)( + x, phi_pyqsp, wires=[0], convention="Wx" # specify angles using convention=`Wx` + ) + qsvt_y_vals.append(np.real(poly_x[0, 0])) + +############################################################################### +# We plot the target function and our approximation generated from QSVT. + +import matplotlib.pyplot as plt + +plt.plot(x_vals, np.array(qsvt_y_vals), label="Re(qsvt)") +plt.plot(np.linspace(s, 1, 50), target_y_vals, label="target") + +plt.vlines(1 / kappa, -1.0, 1.0, linestyle="--", color="grey", label="1/kappa") +plt.vlines(0.0, -1.0, 1.0, color="black") +plt.hlines(0.0, -0.1, 1.0, color="black") + +plt.legend() +plt.show() + +############################################################################### +# Yay! We were able to get an approximation of the function :math:`s \cdot \frac{1}{x}` on the +# domain :math:`[\frac{1}{\kappa}, 1]`. +# +# +# Phase Angles from Optimization +# ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +# The QSVT operation, like all other operations in PennyLane, is fully differentiable. We can +# take advantage of this as an alternate approach to obtaining the phase angles by using gradient-based +# optimization. This method is very versatile because we can use the target function directly +# to generate a polynomial approximation from QSVT. +# +# A single QSVT circuit will produce a transformation that has a fixed parity. We can be +# clever and get a better approximation by using a sum of an even and odd polynomial. +# This is achieved by using a simple linear combination of unitaries +# (`LCU `_). We first split the phase angles into +# two groups (even and odd parity). Next, an ancilla qubit is prepared in equal superposition. +# We apply each QSVT operation, even or odd, conditioned on the ancilla. Finally, the ancilla +# qubit is reset. + + +def sum_even_odd_circ(x, phi, ancilla_wire, wires): + phi1, phi2 = phi[: len(phi) // 2], phi[len(phi) // 2:] + + qml.Hadamard(wires=ancilla_wire) # equal superposition + + # apply even and odd polynomial approx + qml.ctrl(qml.qsvt, control=(ancilla_wire,), control_values=(0,))(x, phi1, wires=wires) + qml.ctrl(qml.qsvt, control=(ancilla_wire,), control_values=(1,))(x, phi2, wires=wires) + + qml.Hadamard(wires=ancilla_wire) # un-prepare superposition + + +############################################################################### +# We now randomly initialize a total of 51 phase angles. This implies that the resulting +# transformation will be a sum of polynomials with degrees 25 and 26, respectively. + +np.random.seed(42) # set seed for reproducibility +phi = np.random.rand(51) + +############################################################################### +# Next, we select a mean-squared error (MSE) loss +# function. The error is computed using samples from the domain :math:`[\frac{1}{\kappa}, 1]` +# where the target function is defined. Since the polynomial produced by the QSVT circuit is +# complex valued, we compare its real value against our target function. We +# ignore the imaginary component for now. + +samples_x = np.linspace(1 / kappa, 1, 100) + +def target_func(x): + return s * (1 / x) + +def loss_func(phi): + sum_square_error = 0 + for x in samples_x: + qsvt_matrix = qml.matrix(sum_even_odd_circ)(x, phi, ancilla_wire="ancilla", wires=[0]) + qsvt_val = qsvt_matrix[0, 0] + sum_square_error += (np.real(qsvt_val) - target_func(x)) ** 2 + + return sum_square_error / len(samples_x) + + +############################################################################### +# Thanks to PennyLane's fully differentiable workflow, we can execute the optimization in just +# a few lines of code: + +# Optimization: +cost = 1 +iter = 0 +opt = qml.AdagradOptimizer(0.1) + +while cost > 0.5e-4: + iter += 1 + phi, cost = opt.step_and_cost(loss_func, phi) + + if iter % 10 == 0 or iter == 1: + print(f"iter: {iter}, cost: {cost}") + + if iter > 100: + print("Iteration limit reached!") + break + +print(f"Completed Optimization! (final cost: {cost})") + +############################################################################### +# Now we plot the results: + +samples_inv = np.linspace(s, 1, 50) +inv_x = [target_func(x) for x in samples_inv] + +samples_x = np.linspace(0, 1, 100) +qsvt_y_vals = [ + np.real(qml.matrix(sum_even_odd_circ)(x, phi, "ancilla", wires=[0])[0, 0]) + for x in samples_x +] + +plt.plot(samples_x, qsvt_y_vals, label="Re(qsvt)") +plt.plot(samples_inv, inv_x, label="target") + +plt.vlines(1 / kappa, -1.0, 1.0, linestyle="--", color="grey", label="1/kappa") +plt.vlines(0.0, -1.0, 1.0, color="black") +plt.hlines(0.0, -0.1, 1.0, color="black") + +plt.legend() +plt.show() + +############################################################################### +# Awesome, we successfully optimized the phase angles! While we used a standard loss function +# and optimizer, users have the freedom to explore any optimizer, loss function, and sampling +# scheme when training the phase angles for QSVT. +# +# Let :math:`\hat{U}_{qsvt}(\vec{\phi}, x)` represent the unitary matrix of the QSVT algorithm. +# Both of the methods above produce phase angles :math:`\vec{\phi}` such that: +# +# .. math:: +# +# Re[\hat{U}_{qsvt}(\vec{\phi}, x)] \approx P(x). +# +# In general, the imaginary part of this transformation will not be zero. We need an operator +# which only applies the real component. Note that we can express the real part of a complex number +# :math:`z` as :math:`Re[z] = \frac{1}{2}(z + z^{*})`. Similarly, for operators this is given by: +# +# .. math:: +# +# \hat{U}_{real}(\vec{\phi}) = \frac{1}{2} \ ( \hat{U}_{qsvt}(\vec{\phi}) + \hat{U}^{\dagger}_{qsvt}(\vec{\phi}) ). +# +# Here we use a two-term LCU to define the quantum function for this operator: + + +def real_u(A, phi): + qml.Hadamard(wires="ancilla1") + + qml.ctrl(sum_even_odd_circ, control=("ancilla1",), control_values=(0,))(A, phi, "ancilla2", [0, 1, 2]) + qml.ctrl(qml.adjoint(sum_even_odd_circ), control=("ancilla1",), control_values=(1,))(A, phi, "ancilla2", [0, 1, 2]) + + qml.Hadamard(wires="ancilla1") + +############################################################################### +# Let's take everything we have learned and apply it to solve a linear system of equations. +# +# Solving a Linear System with QSVT +# --------------------------------- +# Our goal is to solve the equation :math:`A \cdot \vec{x} = \vec{b}`. Let's begin by +# defining the specific matrix :math:`A` and vector :math:`\vec{b}` : +# + +A = np.array( + [ + [0.65713691, -0.05349524, 0.08024556, -0.07242864], + [-0.05349524, 0.65713691, -0.07242864, 0.08024556], + [0.08024556, -0.07242864, 0.65713691, -0.05349524], + [-0.07242864, 0.08024556, -0.05349524, 0.65713691], + ] +) + +b = np.array([1, 2, 3, 4], dtype="complex") +target_x = np.linalg.inv(A) @ b # true solution + +# Normalize states: +norm_b = np.linalg.norm(b) +normalized_b = b / norm_b + +norm_x = np.linalg.norm(target_x) +normalized_x = target_x / norm_x + +############################################################################### +# To solve the linear system we construct a quantum circuit that first prepares the normalized +# vector :math:`\vec{b}` in the working qubit register. Next we call the :code:`real_u(A.T, phi)` +# function that we previously constructed. This is equivalent to applying :math:`s \cdot A^{-1}` to the prepared state. +# Finally, we return the state at the end of the circuit. +# +# The subset of qubits which prepared the :math:`\vec{b}` vector should be transformed to +# represent :math:`\vec{x}` (up to scaling factors): + + +@qml.qnode(qml.device("default.qubit", wires=["ancilla1", "ancilla2", 0, 1, 2])) +def linear_system_solver_circuit(phi): + qml.QubitStateVector(normalized_b, wires=[1, 2]) + real_u(A.T, phi) # invert the singular values of A transpose to get A^-1 + return qml.state() + + +transformed_state = linear_system_solver_circuit(phi)[:4] # first 4 entries of the state +rescaled_computed_x = transformed_state * norm_b / s +normalized_computed_x = rescaled_computed_x / np.linalg.norm(rescaled_computed_x) + +print("target x:", np.round(normalized_x, 3)) +print("computed x:", np.round(normalized_computed_x, 3)) + +############################################################################### +# We have successfully solved the linear system 🎉! Notice that the target state and +# computed state agree well with only some slight deviations. +# +# Conclusion +# ------------------------------- +# In this demo, we showcased the :func:`~.pennylane.qsvt()` functionality in PennyLane. +# We explained how to integrate phase angles computed with external packages and how to use +# PennyLane to optimize the phase angles directly. Finally, we described how to apply QSVT +# to solve an example linear system. +# +# While this demo covered simple example, the general problem of solving linear systems of equations is often the +# bottleneck in many applications from regression analysis in financial modelling to simulating +# fluid dynamics for jet engine design. We hope that PennyLane can help you along the way to +# your next big discovery in quantum algorithms. +# +# References +# ---------- +# +# .. [#qsvt] +# +# András Gilyén, Yuan Su, Guang Hao Low, Nathan Wiebe, +# "Quantum singular value transformation and beyond: exponential improvements for quantum matrix arithmetics", +# `Proceedings of the 51st Annual ACM SIGACT Symposium on the Theory of Computing `__, 2019 +# +# +# .. [#phaseeval] +# +# Dong Y, Meng X, Whaley K, Lin L, +# "Efficient phase-factor evaluation in quantum signal processing", +# `Phys. Rev. A 103, 042419 – `__, 2021 +# +# +# .. [#machineprecision] +# +# Chao R, Ding D, Gilyen A, Huang C, Szegedy M, +# "Finding Angles for Quantum Signal Processing with Machine Precision", +# `arXiv, 2003.02831 `__, 2020 +# +# +# .. [#productdecomp] +# +# Haah J, +# "Product decomposition of periodic functions in quantum signal processing", +# `Quantum 3, 190 `__, 2019 + +############################################################################## +# About the author +# ---------------- +# .. include:: ../_static/authors/jay_soni.txt +# +# .. include:: ../_static/authors/jarrett_smalley.txt diff --git a/demos_quantum-computing.rst b/demos_quantum-computing.rst index de915ef467..c87cb185f8 100644 --- a/demos_quantum-computing.rst +++ b/demos_quantum-computing.rst @@ -167,6 +167,12 @@ such as benchmarking and characterizing quantum processors. :figure: demonstrations/grovers_algorithm/thumbnail_tutorial_grovers_algorithm.png :description: :doc:`demos/tutorial_grovers_algorithm` :tags: quantumcomputing + +.. gallery-item:: + :tooltip: QSVT in Practice + :figure: demonstrations/apply_qsvt/thumbnail_tutorial_QSVT_for_Matrix_Inversion.png + :description: :doc:`demos/tutorial_apply_qsvt` + :tags: quantumcomputing qsvt optimization :html:`
` @@ -179,5 +187,6 @@ IBM, Google, Rigetti, and many more—or on real hardware devices. demos/tutorial_learning_few_data demos/tutorial_geometric_qml demos/tutorial_univariate_qvr + demos/tutorial_contextuality demos/tutorial_equivariant_graph_embedding From 768d59b9dcaa254b35f112acd7edc5c0c80a0f7d Mon Sep 17 00:00:00 2001 From: Guillermo Alonso-Linaje <65235481+KetpuntoG@users.noreply.github.com> Date: Fri, 1 Sep 2023 17:17:11 -0400 Subject: [PATCH 10/24] Update demos_qml.rst (#915) putting the image in the .rst file to be uploaded --- demos_qml.rst | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/demos_qml.rst b/demos_qml.rst index 9f8a77ff68..4b05df205a 100644 --- a/demos_qml.rst +++ b/demos_qml.rst @@ -148,7 +148,7 @@ IBM, Google, Rigetti, and many more—or on real hardware devices. .. gallery-item:: :tooltip: Train a problem-inspired ansatz on a contextuality-inspired dataset - :figure: demonstrations/contextuality/contextuality_thumbnail.png + :figure: demonstrations/contextuality/thumbnail_tutorial_Contextuality.png :description: :doc:`demos/tutorial_contextuality` :tags: jax geometric qml inductive bias From a264b0d5073bebd8a34f07b32a01ebf39fe65f62 Mon Sep 17 00:00:00 2001 From: Guillermo Alonso-Linaje <65235481+KetpuntoG@users.noreply.github.com> Date: Tue, 5 Sep 2023 10:59:11 -0400 Subject: [PATCH 11/24] warning (#916) This demo is currently not working as the Qiskit Runtime VQE program has been withdrawn. Until a decision is made, I will add the warning --- demonstrations/ibm_pennylane.py | 3 +++ 1 file changed, 3 insertions(+) diff --git a/demonstrations/ibm_pennylane.py b/demonstrations/ibm_pennylane.py index 23b00814d1..8066b2beb1 100644 --- a/demonstrations/ibm_pennylane.py +++ b/demonstrations/ibm_pennylane.py @@ -13,6 +13,9 @@ *Authors: Kaur Kristjuhan, Clara Ferreira Cores, Mark Nicholas Jones; Molecular Quantum Solutions (MQS) — Posted: 20 June 2023. Last updated: 20 June 2023.* +.. warning:: + This demo currently does not work as the Qiskit Runtime VQE program has been retired. + Bigger and better quantum computers are built every year. Instead of waiting for the perfect quantum computer to be released, we can already try out the best hardware that exists today. Experimenting on cutting-edge devices helps us understand the technology and improve the way we develop quantum software. PennyLane is a fantastic tool for prototyping From 6c1f8e7ba66020052c6250fd4e58e0313989e2e0 Mon Sep 17 00:00:00 2001 From: Guillermo Alonso-Linaje <65235481+KetpuntoG@users.noreply.github.com> Date: Wed, 6 Sep 2023 10:36:05 -0400 Subject: [PATCH 12/24] Fixing tutorial_apply_qsvt (#912) This is a PR to solve [this issue](https://github.com/PennyLaneAI/qml/issues/910). --------- Co-authored-by: Jay Soni --- demonstrations/tutorial_apply_qsvt.metadata.json | 2 +- demonstrations/tutorial_apply_qsvt.py | 12 +++++++----- 2 files changed, 8 insertions(+), 6 deletions(-) diff --git a/demonstrations/tutorial_apply_qsvt.metadata.json b/demonstrations/tutorial_apply_qsvt.metadata.json index ee9e3abcc8..76ed22a7f5 100644 --- a/demonstrations/tutorial_apply_qsvt.metadata.json +++ b/demonstrations/tutorial_apply_qsvt.metadata.json @@ -9,7 +9,7 @@ } ], "dateOfPublication": "2023-08-22T00:00:00+00:00", - "dateOfLastModification": "2023-08-22T00:00:00+00:00", + "dateOfLastModification": "2023-09-05T00:00:00+00:00", "categories": [ "Quantum Computing", "Algorithms", diff --git a/demonstrations/tutorial_apply_qsvt.py b/demonstrations/tutorial_apply_qsvt.py index bcba41884f..86b2381e9f 100644 --- a/demonstrations/tutorial_apply_qsvt.py +++ b/demonstrations/tutorial_apply_qsvt.py @@ -288,16 +288,17 @@ def loss_func(phi): # # .. math:: # -# \hat{U}_{real}(\vec{\phi}) = \frac{1}{2} \ ( \hat{U}_{qsvt}(\vec{\phi}) + \hat{U}^{\dagger}_{qsvt}(\vec{\phi}) ). +# \hat{U}_{real}(\vec{\phi}) = \frac{1}{2} \ ( \hat{U}_{qsvt}(\vec{\phi}) + \hat{U}^{*}_{qsvt}(\vec{\phi}) ). # -# Here we use a two-term LCU to define the quantum function for this operator: +# Here we use a two-term LCU to define the quantum function for this operator. We obtain the complex +# conjugate of :math:`\hat{U}_{qsvt}` by taking the adjoint of the operator block-encoding :math:`A^{T}`: def real_u(A, phi): qml.Hadamard(wires="ancilla1") qml.ctrl(sum_even_odd_circ, control=("ancilla1",), control_values=(0,))(A, phi, "ancilla2", [0, 1, 2]) - qml.ctrl(qml.adjoint(sum_even_odd_circ), control=("ancilla1",), control_values=(1,))(A, phi, "ancilla2", [0, 1, 2]) + qml.ctrl(qml.adjoint(sum_even_odd_circ), control=("ancilla1",), control_values=(1,))(A.T, phi, "ancilla2", [0, 1, 2]) qml.Hadamard(wires="ancilla1") @@ -306,8 +307,9 @@ def real_u(A, phi): # # Solving a Linear System with QSVT # --------------------------------- -# Our goal is to solve the equation :math:`A \cdot \vec{x} = \vec{b}`. Let's begin by -# defining the specific matrix :math:`A` and vector :math:`\vec{b}` : +# Our goal is to solve the equation :math:`A \cdot \vec{x} = \vec{b}`. This method assumes +# the matrix we will invert is hermitian. Let's begin by defining the specific matrix :math:`A` +# and vector :math:`\vec{b}` : # A = np.array( From 161db02c434899c33d5dcd5ca1d47bf19ed6233b Mon Sep 17 00:00:00 2001 From: Guillermo Alonso-Linaje <65235481+KetpuntoG@users.noreply.github.com> Date: Fri, 8 Sep 2023 09:44:55 -0400 Subject: [PATCH 13/24] testing style (#918) Pl style of matplotlib does not work in the repository. Testing locally has shown that updating matplotlib does work. --- README.md | 2 +- demonstrations/tutorial_contextuality.py | 10 +- poetry.lock | 166 +++++++++++++++++------ pyproject.toml | 2 +- 4 files changed, 131 insertions(+), 49 deletions(-) diff --git a/README.md b/README.md index ffd7b2a7fc..0a4a5a7314 100644 --- a/README.md +++ b/README.md @@ -24,7 +24,7 @@ programming](https://en.wikipedia.org/wiki/Differentiable_programming) of quantu The content consists of three learning hubs and three additional areas: -- Learning hubs: +- Learning hubs: + [What is quantum computing?](https://pennylane.ai/qml/what-is-quantum-computing.html) Understand what quantum computers can do and how we can make them do it. + [What is quantum machine learning?](https://pennylane.ai/qml/whatisqml.html) Understand what quantum computing means for machine learning. diff --git a/demonstrations/tutorial_contextuality.py b/demonstrations/tutorial_contextuality.py index 842958be48..66857f42ec 100644 --- a/demonstrations/tutorial_contextuality.py +++ b/demonstrations/tutorial_contextuality.py @@ -713,13 +713,15 @@ def optimise_model(model, nstep, lr, weights): import matplotlib.pyplot as plt +plt.style.use('pennylane.drawer.plot') + # subplots fig, (ax1, ax2) = plt.subplots(nrows=2, ncols=1, figsize=(8, 10)) fig.tight_layout(pad=10.0) # KL divergence -ax1.plot(plot_model[0], plot_model[2], color="red", label="biased model") -ax1.plot(plot_genereic[0], plot_genereic[2], color="blue", label="generic model") +ax1.plot(plot_model[0], plot_model[2], label="biased model") +ax1.plot(plot_genereic[0], plot_genereic[2], label="generic model") ax1.set_yscale("log") ax1.set_ylabel("KL divergence (test)") @@ -727,8 +729,8 @@ def optimise_model(model, nstep, lr, weights): ax1.legend() # negative log likelihood -ax2.plot(plot_model[0], plot_model[1], color="red") -ax2.plot(plot_genereic[0], plot_genereic[1], color="blue") +ax2.plot(plot_model[0], plot_model[1]) +ax2.plot(plot_genereic[0], 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7b470fef1f..a2105133d6 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -45,7 +45,7 @@ pennylane-qulacs = "0.32.0" ########################################################## -matplotlib = "3.5.3" +matplotlib = "3.7.2" jax = "0.4.12" jaxlib = "0.4.12" jaxopt = "0.5.5" From c295c876ce7d4d50f1c89e2d4889d717a7084966 Mon Sep 17 00:00:00 2001 From: Rashid N H M <95639609+rashidnhm@users.noreply.github.com> Date: Fri, 8 Sep 2023 12:28:02 -0400 Subject: [PATCH 14/24] Fix bug in upload-json where searchindex.js was not being synced to the correct bucket (#920) --- .github/workflows/upload-json.yml | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/.github/workflows/upload-json.yml b/.github/workflows/upload-json.yml index 5d95266fd2..3a20193293 100644 --- a/.github/workflows/upload-json.yml +++ b/.github/workflows/upload-json.yml @@ -49,6 +49,7 @@ jobs: AWS_ACCESS_KEY_ID: ${{ secrets.aws_access_key_id }} AWS_SECRET_ACCESS_KEY: ${{ secrets.aws_secret_access_key }} AWS_S3_BUCKET_ID: ${{ secrets.aws_json_s3_bucket_id }} + AWS_S3_HTML_BUCKET_ID: ${{ secrets.aws_html_s3_bucket_id }} AWS_S3_BUCKET_DIR: ${{ inputs.aws_s3_bucket_dir }} steps: @@ -102,7 +103,7 @@ jobs: run: aws s3 sync s3://$AWS_S3_BUCKET_ID/$AWS_S3_BUCKET_DIR s3://$AWS_S3_BUCKET_ID/${{ steps.branch_name.outputs.name }} --delete - name: Sync searchindex to HTML Bucket - run: aws s3 cp searchindex.js s3://$AWS_S3_BUCKET_ID/qml/searchindex.js + run: aws s3 cp searchindex.js s3://$AWS_S3_HTML_BUCKET_ID/qml/searchindex.js trigger-website-build: runs-on: ubuntu-latest From c432641663681c9770d97f66c80c3c9e3689964c Mon Sep 17 00:00:00 2001 From: Paul Finlay <50180049+doctorperceptron@users.noreply.github.com> Date: Fri, 8 Sep 2023 13:10:08 -0400 Subject: [PATCH 15/24] Deploy prod to staging (#917) Depends on PR #920 being merged first. This PR adds two new features to the existing CI workflows: 1. Merges to `master` will now deploy the demos to the [staging environment](https://staging.pennylane.ai) rather than into production. 2. The demos from `master` can be deployed to [production](https://pennylane.ai) via a manually triggered workflow (`deploy-demos-to-prod`). The purpose for this is so that after reviewing new demos via PR preview, they can be QA-ed one final time on the staging website to ensure everything works correctly before promoting them to production. This PR requires [this pennylane.ai-react PR](https://github.com/XanaduAI/pennylane.ai-react/pull/541) to be merged as well. NOTE: Won't update the README as this is for external users. --- .github/workflows/build-json.yml | 21 +---------- .github/workflows/deploy-demos-to-prod.yml | 41 ++++++++++++++++++++++ 2 files changed, 42 insertions(+), 20 deletions(-) create mode 100644 .github/workflows/deploy-demos-to-prod.yml diff --git a/.github/workflows/build-json.yml b/.github/workflows/build-json.yml index b292e1ad4a..1436a0fcaf 100644 --- a/.github/workflows/build-json.yml +++ b/.github/workflows/build-json.yml @@ -22,27 +22,8 @@ jobs: skip_sphinx_build_file_aggregation: false sphinx_build_output_format: json - upload-production: - if: github.ref_name == 'master' - needs: - - build - uses: ./.github/workflows/upload-json.yml - with: - branch: ${{ github.ref }} - aws_s3_bucket_dir: commits/${{ github.ref_name }}-${{ github.sha }} - artifact_name: json.zip - qml_react_webhook_event_type: build-pl-site-main - - secrets: - aws_region: ${{ secrets.AWS_REGION }} - aws_access_key_id: ${{ secrets.PL_SITE_PROD_NON_REACT_ACCESS_KEY_ID }} - aws_secret_access_key: ${{ secrets.PL_SITE_PROD_NON_REACT_SECRET_ACCESS_KEY }} - aws_json_s3_bucket_id: ${{ secrets.PL_SITE_PROD_QML_JSON_S3_BUCKET_ID }} - aws_html_s3_bucket_id: ${{ secrets.PL_SITE_PROD_S3_BUCKET_NAME }} - qml_react_pat_token: ${{ secrets.PL_SITE_PROD_QML_REACT_PAT }} - upload-staging: - if: github.ref_name == 'dev' + if: github.ref_name == 'master' needs: - build uses: ./.github/workflows/upload-json.yml diff --git a/.github/workflows/deploy-demos-to-prod.yml b/.github/workflows/deploy-demos-to-prod.yml new file mode 100644 index 0000000000..ba81e793db --- /dev/null +++ b/.github/workflows/deploy-demos-to-prod.yml @@ -0,0 +1,41 @@ +name: Deploy Demos to Production +on: + workflow_dispatch: + +jobs: + deploy-demos-to-production: + runs-on: ubuntu-latest + + env: + AWS_REGION: ${{ secrets.aws_region }} + + steps: + - name: Pull staging bucket + env: + AWS_ACCESS_KEY_ID: ${{ secrets.PL_SITE_STAGING_NON_REACT_ACCESS_KEY_ID }} + AWS_SECRET_ACCESS_KEY: ${{ secrets.PL_SITE_STAGING_NON_REACT_SECRET_ACCESS_KEY }} + AWS_S3_BUCKET_ID: ${{ secrets.PL_SITE_STAGING_QML_JSON_S3_BUCKET_ID }} + AWS_S3_HTML_BUCKET_ID: ${{ secrets.PL_SITE_STAGING_S3_BUCKET_NAME }} + run: | + aws s3 cp s3://$AWS_S3_BUCKET_ID/master ./local-master + aws s3 cp s3://$AWS_S3_HTML_BUCKET_ID/qml/searchindex.js searchindex.js + + - name: Push production bucket + env: + AWS_ACCESS_KEY_ID: ${{ secrets.PL_SITE_PROD_NON_REACT_ACCESS_KEY_ID }} + AWS_SECRET_ACCESS_KEY: ${{ secrets.PL_SITE_PROD_NON_REACT_SECRET_ACCESS_KEY }} + AWS_S3_BUCKET_ID: ${{ secrets.PL_SITE_PROD_QML_JSON_S3_BUCKET_ID }} + AWS_S3_HTML_BUCKET_ID: ${{ secrets.PL_SITE_PROD_S3_BUCKET_NAME }} + run: | + aws s3 sync ./local-master s3://$AWS_S3_BUCKET_ID/master --delete + aws s3 cp searchindex.js s3://$AWS_S3_HTML_BUCKET_ID/qml/searchindex.js + + - name: Trigger production website build + uses: peter-evans/repository-dispatch@v2 + env: + RUN_URL: ${{ github.server_url }}/${{ github.repository }}/actions/runs/${{ github.run_id }} + with: + token: ${{ secrets.PL_SITE_PROD_QML_REACT_PAT }} + repository: XanaduAI/pennylane.ai-react + event-type: build-pl-site-main + client-payload: '{"actor": "${{ github.actor }}", "triggering_actor": "${{ github.triggering_actor }}", "source_run_url": "${{ env.RUN_URL }}"}' From 1cf2672d14193ef887f511a9addafeca1eb10da3 Mon Sep 17 00:00:00 2001 From: David Wakeham <113920759+branedrane@users.noreply.github.com> Date: Mon, 11 Sep 2023 07:48:40 -0700 Subject: [PATCH 16/24] Add Fourier explainer demo (#858) **Title:** Fourier explainer demo **Summary:** A tutorial on circuits as Fourier series and the parameter-shift rule, told through interactive javascript visualizations. **Relevant references:** - General parameter-shift rules for quantum gradients (Weirichs, Izaac, Wang, Lin) - The effect of data encoding on the expressive power of variational quantum machine learning models (Schuld, Sweke, Myer) - Quantum models as Fourier series (PL demo by Schuld, Myer) **Possible Drawbacks:** Although this is code-free by design, to justify inclusion as a PL demo perhaps it should be supplemented by references to other PL demos or documentation. **Related GitHub Issues:** N/A --------- Co-authored-by: Josh Izaac Co-authored-by: Guillermo Alonso-Linaje <65235481+KetpuntoG@users.noreply.github.com> Co-authored-by: Isaac De Vlugt <34751083+isaacdevlugt@users.noreply.github.com> --- .../circuits_as_fourier_series/sketch.js | 1031 +++++++++++++++++ .../circuits_as_fourier_series/src/12-l4.png | Bin 0 -> 13411 bytes .../src/fourier0-1.png | Bin 0 -> 53884 bytes .../src/fourier0-2.png | Bin 0 -> 56609 bytes .../src/fourier0-3.png | Bin 0 -> 57111 bytes .../src/fourier0.png | Bin 0 -> 71390 bytes .../src/fourier1-0.png | Bin 0 -> 56738 bytes .../src/fourier1-1.png | Bin 0 -> 55655 bytes .../src/fourier10-0000.png | Bin 0 -> 117869 bytes .../src/fourier10-0001.png | Bin 0 -> 117742 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_static/demos/circuits_as_fourier_series/src/fourier8_5_00.png create mode 100644 _static/demos/circuits_as_fourier_series/src/fourier8_5_01.png create mode 100644 _static/demos/circuits_as_fourier_series/src/fourier8_5_10.png create mode 100644 _static/demos/circuits_as_fourier_series/src/fourier8_5_11.png create mode 100644 _static/demos/circuits_as_fourier_series/src/fourier9-0000.png create mode 100644 _static/demos/circuits_as_fourier_series/src/fourier9-0001.png create mode 100644 _static/demos/circuits_as_fourier_series/src/fourier9-0010.png create mode 100644 _static/demos/circuits_as_fourier_series/src/fourier9-0011.png create mode 100644 _static/demos/circuits_as_fourier_series/src/fourier9-0100.png create mode 100644 _static/demos/circuits_as_fourier_series/src/fourier9-0101.png create mode 100644 _static/demos/circuits_as_fourier_series/src/fourier9-0110.png create mode 100644 _static/demos/circuits_as_fourier_series/src/fourier9-0111.png create mode 100644 _static/demos/circuits_as_fourier_series/src/fourier9-1000.png create mode 100644 _static/demos/circuits_as_fourier_series/src/fourier9-1001.png create mode 100644 _static/demos/circuits_as_fourier_series/src/fourier9-1010.png create mode 100644 _static/demos/circuits_as_fourier_series/src/fourier9-1011.png create mode 100644 _static/demos/circuits_as_fourier_series/src/fourier9-1100.png create mode 100644 _static/demos/circuits_as_fourier_series/src/fourier9-1101.png create mode 100644 _static/demos/circuits_as_fourier_series/src/fourier9-1110.png create mode 100644 _static/demos/circuits_as_fourier_series/src/fourier9-1111.png create mode 100644 _static/large_demo_thumbnails/thumbnail_large_circuits_as_fourier_series.png create mode 100644 demonstrations/circuits_as_fourier_series.metadata.json create mode 100644 demonstrations/circuits_as_fourier_series.py create mode 100644 demonstrations/circuits_as_fourier_series/thumbnail_circuits_as_fourier_series.png diff --git a/_static/demos/circuits_as_fourier_series/sketch.js b/_static/demos/circuits_as_fourier_series/sketch.js new file mode 100644 index 0000000000..8d339f93db --- /dev/null +++ b/_static/demos/circuits_as_fourier_series/sketch.js @@ -0,0 +1,1031 @@ +var url_prefix = '../_static/demos/circuits_as_fourier_series/src/'; + +// fetch(url_prefix + 'fourier0-1.png', {method: 'HEAD'}).then(res => { +// if (!res.ok) { +// url_prefix = '../_static/demos/circuits_as_fourier_series/src/'; +// } +// }); + + +///// Sketch 0 + +var sketch0_1 = function(p) { + var mod = 0.5; + var width = 600*mod, height = 400*mod; + + p.setup = function() { + const canvas1 = p.createCanvas(width, height); + bg0_1 = p.loadImage(url_prefix + 'fourier0-1.png'); + } + + p.draw = function() { + p.background(bg0_1); + } +} + +var myp501 = new p5(sketch0_1, 'sketch0_1'); + +var sketch0_2 = function(p) { + var mouse = 0; + var mod = 0.65; + var width = 600*mod, height = 400*mod; + + p.setup = function() { + const canvas1 = p.createCanvas(width, height); + bg0_2 = p.loadImage(url_prefix + 'fourier0-2.png'); + bg0_3 = p.loadImage(url_prefix + 'fourier0-3.png'); + } + + p.draw = function() { + if (mouse == 0) { + p.background(bg0_2); + } + if (mouse == 1) { + p.background(bg0_3); + } + } + + p.mouseClicked = function() { + if ((p.mouseX > 0) && (p.mouseX < width) && (p.mouseY > 0) && (p.mouseY < height)) { + mouse = (mouse + 1)%2; + } + } +} + +var myp502 = new p5(sketch0_2, 'sketch0_2'); + +///// Sketch 1 + +var sketch1 = function(p) { + var mouse = 0; + var mod = 0.65; + var width = 600*mod, height = 400*mod; + + p.setup = function() { + const canvas1 = p.createCanvas(width, height); + bg1_0 = p.loadImage(url_prefix + 'fourier1-0.png'); + bg1_1 = p.loadImage(url_prefix + 'fourier1-1.png'); + } + + p.draw = function() { + if (mouse == 0) { + p.background(bg1_0); + } + if (mouse == 1) { + p.background(bg1_1); + } + } + + p.mouseClicked = function() { + if ((p.mouseX > 0) && (p.mouseX < width) && (p.mouseY > 0) && (p.mouseY < height)) { + mouse = (mouse + 1)%2; + } + } +} + +var myp51 = new p5(sketch1, 'sketch1'); + + +///// Sketch 2 + +var sketch2 = function(p) { + var mouse = 0; + var i, omega, gamma, shift; + var gamma = 1; + var kappa = 0; + var mod = 0.75; + var width = 600*mod, height = 400*mod; + var steps = 100, start = 95, end = 370, lift = 232, freq = 10, amp = 30; + + p.setup = function() { + const canvas1 = p.createCanvas(width, height); + + bg2_0 = p.loadImage(url_prefix + 'fourier2-0.png'); + bg2_1 = p.loadImage(url_prefix + 'fourier2-1.png'); + } + + p.draw = function() { + if (mouse == 0) { + p.background(bg2_0); + omega = -gamma; + shift = 0; + } + if (mouse == 1) { + p.background(bg2_1); + omega = gamma; + shift = 0; + } + + let step = (end - start)/steps; + + for (i = 0; i < steps; i++) { + let x = i*step + start; + p.stroke(112, 206, 255); + p.strokeWeight(2); + p.line(x, lift - amp*p.cos(shift + omega*(x-start)/freq), x + step, lift - amp*p.cos(shift + omega*((x-start) + step)/freq)); + } + + for (i = 0; i < steps; i++) { + let x = i*step + start; + p.stroke(181, 242, 237); + p.strokeWeight(2); + p.line(x, lift - amp*p.sin(shift + omega*(x-start)/freq), x + step, lift - amp*p.sin(shift + omega*((x-start) + step)/freq)); + } + + if ((p.mouseX > 140) && (p.mouseX < 260) && (p.mouseY > 25) && (p.mouseY < 120)) { + gamma = 1 + (120 - p.mouseY)/(120 - 25); + kappa = 2*3.142*(260 - p.mouseX)/(260 - 140); + } + } + + p.mouseClicked = function() { + if ((p.mouseX > 140) && (p.mouseX < 260) && (p.mouseY > 25) && (p.mouseY < 120)) { + mouse = (mouse + 1)%2; + } + } +} + +var myp52 = new p5(sketch2, 'sketch2'); + + +///// Sketch 3 + +var sketch3 = function(p) { + var mouse = 0; + var i, omega, amp, shift; + var gamma = 1; + var kappa = 0; + var mod = 0.75; + var width = 600*mod, height = 400*mod; + var steps = 100, start = 100, end = 370, lift = 232, freq = 10, amp0 = 30, amp1 = 30; + + p.setup = function() { + const canvas1 = p.createCanvas(width, height); + + bg3_0 = p.loadImage(url_prefix + 'fourier3-0.png'); + bg3_1 = p.loadImage(url_prefix + 'fourier3-1.png'); + } + + p.draw = function() { + let step = (end - start)/steps; + + if (mouse == 0) { + p.background(bg3_0); + omega = -gamma; + shift = 0; + amp = amp0; + } + + if (mouse == 1) { + p.background(bg3_1); + omega = gamma; + shift = 0; + amp = amp1; + } + + for (i = 0; i < steps; i++) { + let x = i*step + start; + p.stroke(112, 206, 255); + p.strokeWeight(2); + p.line(x, lift - amp*p.cos(shift + omega*(x-start)/freq), x + step, lift - amp*p.cos(shift + omega*((x-start) + step)/freq)); + } + + for (i = 0; i < steps; i++) { + let x = i*step + start; + p.stroke(181, 242, 237); + p.strokeWeight(2); + p.line(x, lift - amp*p.sin(shift + omega*(x-start)/freq), x + step, lift - amp*p.sin(shift + omega*((x-start) + step)/freq)); + } + + if ((p.mouseX > 160) && (p.mouseX < 275) && (p.mouseY > 20) && (p.mouseY < 115)) { + gamma = 1 + (120 - p.mouseY)/(120 - 25); + kappa = 2*3.142*(275 - p.mouseX)/(275 - 160); + } + + if ((p.mouseX > 135) && (p.mouseX < 160) && (p.mouseY > 20) && (p.mouseY < 115)) { + amp0 = -60*(p.mouseY - 115)/(115-20); + amp1 = 60*(p.mouseY - 20)/(115-20); + } + } + + p.mouseClicked = function() { + if ((p.mouseX > 160) && (p.mouseX < 275) && (p.mouseY > 20) && (p.mouseY < 115)) { + mouse = (mouse + 1)%2; + } + } +} + +var myp53 = new p5(sketch3, 'sketch3'); + +///// Sketch 4 + +var sketch4 = function(p) { + var mouse1 = 0, mouse2 = 0; + var i, omega; + var gamma = 1; + var kappa = 0; + var mod = 0.75; + var width = 600*mod, height = 400*mod; + var steps = 200, start = 80, end = 370, lift = 232, freq = 10, amp = 30, amp0 = 30, amp1 = 30; + var updown = 0, shift = 0; + + p.setup = function() { + const canvas1 = p.createCanvas(width, height); + + bg4_00 = p.loadImage(url_prefix + 'fourier4-00.png'); + bg4_01 = p.loadImage(url_prefix + 'fourier4-01.png'); + bg4_10 = p.loadImage(url_prefix + 'fourier4-10.png'); + bg4_11 = p.loadImage(url_prefix + 'fourier4-11.png'); + } + + p.draw = function() { + let step = (end - start)/steps; + + str1 = mouse1.toString(); + str2 = mouse2.toString(); + picName = "bg4_" + str1 + str2; + p.background(eval(picName)); + + if ((p.mouseX > 107) && (p.mouseX < 187) && (p.mouseY > 40) && (p.mouseY < 100)) { + gamma = 1 + (100 - p.mouseY)/(100 - 40); + kappa = 2*3.142*(187 - p.mouseX)/(187 - 107); + } + + if ((p.mouseX > 90) && (p.mouseX < 107) && (p.mouseY > 40) && (p.mouseY < 100)) { + amp0 = -15*(p.mouseY - 100)/(100-40); + amp1 = 15*(p.mouseY -40)/(100-40); + amp = amp0*amp1; + } + + if ((p.mouseX > 210) && (p.mouseX < 235) && (p.mouseY > 45) && (p.mouseY < 90)) { + updown = 0.07*((p.mouseY - (45 + 90)/2) + 1.5); + } + + if ((mouse1 + mouse2) == 0) { + + for (i = 0; i < steps; i++) { + let x = i*step + start; + p.stroke(112, 206, 255); + p.strokeWeight(2); + p.line(x, lift + amp0*updown, x + step, lift + amp0*updown); + } + } + + if ((mouse1 + mouse2) == 2) { + + for (i = 0; i < steps; i++) { + let x = i*step + start; + p.stroke(181, 242, 237); + p.strokeWeight(2); + p.line(x, lift + amp1*updown, x + step, lift + amp1*updown); + } + } + + if ((mouse1 + mouse2) == 1) { + if (mouse1 == 1) { + omega = -2*gamma; + shift = 0; + + } + if (mouse1 == 0) { + omega = 2*gamma; + shift = 0; + } + + for (i = 0; i < steps; i++) { + let x = i*step + start; + p.stroke(112, 206, 255); + p.strokeWeight(2); + p.line(x, lift - amp*p.cos(shift + omega*(x-start)/freq), x + step, lift - amp*p.cos(shift + omega*((x-start) + step)/freq)); + } + + for (i = 0; i < steps; i++) { + let x = i*step + start; + p.stroke(181, 242, 237); + p.strokeWeight(2); + p.line(x, lift + amp*p.sin(shift + omega*(x-start)/freq), x + step, lift + amp*p.sin(shift + omega*((x-start) + step)/freq)); + } + } + } + + p.mouseClicked = function() { + if ((p.mouseX > 107) && (p.mouseX < 187) && (p.mouseY > 50) && (p.mouseY < 120)) { + mouse1 = (mouse1 + 1)%2; + } + if ((p.mouseX > 260) && (p.mouseX < 340) && (p.mouseY > 50) && (p.mouseY < 120)) { + mouse2 = (mouse2 + 1)%2; + } + } +} + +var myp54 = new p5(sketch4, 'sketch4'); + + +///// Sketch 5 + +var sketch5 = function(p) { + var mouse1 = 0; + var mod = 0.75; + var width = 600*mod, height = 400*mod; + + p.setup = function() { + const canvas1 = p.createCanvas(width, height); + + bg5_0 = p.loadImage(url_prefix + 'fourier5-0.png'); + bg5_1 = p.loadImage(url_prefix + 'fourier5-1.png'); + } + + p.draw = function() { + str1 = mouse1.toString(); + picName = "bg5_" + str1; + p.background(eval(picName)); + } + + p.mouseClicked = function() { + if ((p.mouseX > 120) && (p.mouseX < 240) && (p.mouseY > 40) && (p.mouseY < 145)) { + mouse1 = (mouse1 + 1)%2; + } + } +} + +var myp55 = new p5(sketch5, 'sketch5'); + + +///// Sketch 6 + +var sketch6 = function(p) { + var mouse1 = 0, mouse2 = 0; + var mod = 0.75; + var width = 600*mod, height = 400*mod; + + p.setup = function() { + const canvas1 = p.createCanvas(width, height); + + bg6_00 = p.loadImage(url_prefix + 'fourier6-00.png'); + bg6_01 = p.loadImage(url_prefix + 'fourier6-01.png'); + bg6_10 = p.loadImage(url_prefix + 'fourier6-10.png'); + bg6_11 = p.loadImage(url_prefix + 'fourier6-11.png'); + } + + p.draw = function() { + picName = "bg6_" + mouse1.toString() + mouse2.toString(); + p.background(eval(picName)); + } + + p.mouseClicked = function() { + if ((p.mouseX > 80) && (p.mouseX < 183) && (p.mouseY > 20) && (p.mouseY < 110)) { + mouse1 = (mouse1 + 1)%2; + } + if ((p.mouseX > 80) && (p.mouseX < 183) && (p.mouseY > 137) && (p.mouseY < 225)) { + mouse2 = (mouse2 + 1)%2; + } + } +} + +var myp56 = new p5(sketch6, 'sketch6'); + +///// Sketch 7 + +var sketch7 = function(p) { + var mouse1 = 0, mouse2 = 0, mouse3 = 0, mouse4 = 0; + var mod = 0.75; + var width = 600*mod, height = 400*mod; + + p.setup = function() { + const canvas1 = p.createCanvas(width, height); + + bg7_1111 = p.loadImage(url_prefix + 'fourier7-0000.png'); + bg7_1110 = p.loadImage(url_prefix + 'fourier7-0001.png'); + bg7_1101 = p.loadImage(url_prefix + 'fourier7-0010.png'); + bg7_1100 = p.loadImage(url_prefix + 'fourier7-0011.png'); + bg7_1011 = p.loadImage(url_prefix + 'fourier7-0100.png'); + bg7_1010 = p.loadImage(url_prefix + 'fourier7-0101.png'); + bg7_1001 = p.loadImage(url_prefix + 'fourier7-0110.png'); + bg7_1000 = p.loadImage(url_prefix + 'fourier7-0111.png'); + bg7_0111 = p.loadImage(url_prefix + 'fourier7-1000.png'); + bg7_0110 = p.loadImage(url_prefix + 'fourier7-1001.png'); + bg7_0101 = p.loadImage(url_prefix + 'fourier7-1010.png'); + bg7_0100 = p.loadImage(url_prefix + 'fourier7-1011.png'); + bg7_0011 = p.loadImage(url_prefix + 'fourier7-1100.png'); + bg7_0010 = p.loadImage(url_prefix + 'fourier7-1101.png'); + bg7_0001 = p.loadImage(url_prefix + 'fourier7-1110.png'); + bg7_0000 = p.loadImage(url_prefix + 'fourier7-1111.png'); + + } + + p.draw = function() { + str1 = mouse1.toString(); + str2 = mouse2.toString(); + str3 = mouse3.toString(); + str4 = mouse4.toString(); + picName = "bg7_" + str1 + str2 + str3 + str4; + p.background(eval(picName)); + } + + p.mouseClicked = function() { + if ((p.mouseX > 72) && (p.mouseX < 160) && (p.mouseY > 20) && (p.mouseY < 97)) { + mouse1 = (mouse1 + 1)%2; + } + if ((p.mouseX > 285) && (p.mouseX < 373) && (p.mouseY > 20) && (p.mouseY < 97)) { + mouse2 = (mouse2 + 1)%2; + } + if ((p.mouseX > 72) && (p.mouseX < 160) && (p.mouseY > 115) && (p.mouseY < 192)) { + mouse3 = (mouse3 + 1)%2; + } + if ((p.mouseX > 285) && (p.mouseX < 373) && (p.mouseY > 115) && (p.mouseY < 192)) { + mouse4 = (mouse4 + 1)%2; + } + } +} + +var myp57 = new p5(sketch7, 'sketch7'); +///// Sketch 8 + +var sketch8 = function(p) { + var mouse1 = 0, mouse2 = 0, mouse3 = 0, mouse4 = 0; + var mod = 0.75; + var width = 600*mod, height = 400*mod; + + p.setup = function() { + const canvas1 = p.createCanvas(width, height); + + bg8_1111 = p.loadImage(url_prefix + 'fourier8-0000.png'); + bg8_1110 = p.loadImage(url_prefix + 'fourier8-0001.png'); + bg8_1101 = p.loadImage(url_prefix + 'fourier8-0010.png'); + bg8_1100 = p.loadImage(url_prefix + 'fourier8-0011.png'); + bg8_1011 = p.loadImage(url_prefix + 'fourier8-0100.png'); + bg8_1010 = p.loadImage(url_prefix + 'fourier8-0101.png'); + bg8_1001 = p.loadImage(url_prefix + 'fourier8-0110.png'); + bg8_1000 = p.loadImage(url_prefix + 'fourier8-0111.png'); + bg8_0111 = p.loadImage(url_prefix + 'fourier8-1000.png'); + bg8_0110 = p.loadImage(url_prefix + 'fourier8-1001.png'); + bg8_0101 = p.loadImage(url_prefix + 'fourier8-1010.png'); + bg8_0100 = p.loadImage(url_prefix + 'fourier8-1011.png'); + bg8_0011 = p.loadImage(url_prefix + 'fourier8-1100.png'); + bg8_0010 = p.loadImage(url_prefix + 'fourier8-1101.png'); + bg8_0001 = p.loadImage(url_prefix + 'fourier8-1110.png'); + bg8_0000 = p.loadImage(url_prefix + 'fourier8-1111.png'); + + } + + p.draw = function() { + str1 = mouse1.toString(); + str2 = mouse2.toString(); + str3 = mouse3.toString(); + str4 = mouse4.toString(); + picName = "bg8_" + str1 + str2 + str3 + str4; + p.background(eval(picName)); + } + + p.mouseClicked = function() { + if ((p.mouseX > 72) && (p.mouseX < 160) && (p.mouseY > 20) && (p.mouseY < 97)) { + mouse1 = (mouse1 + 1)%2; + } + if ((p.mouseX > 285) && (p.mouseX < 373) && (p.mouseY > 20) && (p.mouseY < 97)) { + mouse2 = (mouse2 + 1)%2; + } + if ((p.mouseX > 72) && (p.mouseX < 160) && (p.mouseY > 115) && (p.mouseY < 192)) { + mouse3 = (mouse3 + 1)%2; + } + if ((p.mouseX > 285) && (p.mouseX < 373) && (p.mouseY > 115) && (p.mouseY < 192)) { + mouse4 = (mouse4 + 1)%2; + } + } +} + +var myp58 = new p5(sketch8, 'sketch8'); + + +///// Sketch 9 + +var sketch9 = function(p) { + var mouse1 = 0, mouse2 = 0, mouse3 = 0, mouse4 = 0; + var mod = 0.75; + var width = 600*mod, height = 400*mod; + + p.setup = function() { + const canvas1 = p.createCanvas(width, height); + + bg9_1111 = p.loadImage(url_prefix + 'fourier9-0000.png'); + bg9_1110 = p.loadImage(url_prefix + 'fourier9-0001.png'); + bg9_1101 = p.loadImage(url_prefix + 'fourier9-0010.png'); + bg9_1100 = p.loadImage(url_prefix + 'fourier9-0011.png'); + bg9_1011 = p.loadImage(url_prefix + 'fourier9-0100.png'); + bg9_1010 = p.loadImage(url_prefix + 'fourier9-0101.png'); + bg9_1001 = p.loadImage(url_prefix + 'fourier9-0110.png'); + bg9_1000 = p.loadImage(url_prefix + 'fourier9-0111.png'); + bg9_0111 = p.loadImage(url_prefix + 'fourier9-1000.png'); + bg9_0110 = p.loadImage(url_prefix + 'fourier9-1001.png'); + bg9_0101 = p.loadImage(url_prefix + 'fourier9-1010.png'); + bg9_0100 = p.loadImage(url_prefix + 'fourier9-1011.png'); + bg9_0011 = p.loadImage(url_prefix + 'fourier9-1100.png'); + bg9_0010 = p.loadImage(url_prefix + 'fourier9-1101.png'); + bg9_0001 = p.loadImage(url_prefix + 'fourier9-1110.png'); + bg9_0000 = p.loadImage(url_prefix + 'fourier9-1111.png'); + + } + + p.draw = function() { + str1 = mouse1.toString(); + str2 = mouse2.toString(); + str3 = mouse3.toString(); + str4 = mouse4.toString(); + picName = "bg9_" + str1 + str2 + str3 + str4; + p.background(eval(picName)); + } + + p.mouseClicked = function() { + if ((p.mouseX > 72) && (p.mouseX < 160) && (p.mouseY > 20) && (p.mouseY < 97)) { + mouse1 = (mouse1 + 1)%2; + } + if ((p.mouseX > 285) && (p.mouseX < 373) && (p.mouseY > 20) && (p.mouseY < 97)) { + mouse2 = (mouse2 + 1)%2; + } + if ((p.mouseX > 72) && (p.mouseX < 160) && (p.mouseY > 115) && (p.mouseY < 192)) { + mouse3 = (mouse3 + 1)%2; + } + if ((p.mouseX > 285) && (p.mouseX < 373) && (p.mouseY > 115) && (p.mouseY < 192)) { + mouse4 = (mouse4 + 1)%2; + } + } +} + +var myp59 = new p5(sketch9, 'sketch9'); + +///// Sketch 10 + +var sketch10 = function(p) { + var mouse1 = 0, mouse2 = 0, mouse3 = 0, mouse4 = 0; + var mod = 0.75; + var width = 600*mod, height = 400*mod; + + p.setup = function() { + const canvas1 = p.createCanvas(width, height); + + bg10_1111 = p.loadImage(url_prefix + 'fourier10-0000.png'); + bg10_1110 = p.loadImage(url_prefix + 'fourier10-0001.png'); + bg10_1101 = p.loadImage(url_prefix + 'fourier10-0010.png'); + bg10_1100 = p.loadImage(url_prefix + 'fourier10-0011.png'); + bg10_1011 = p.loadImage(url_prefix + 'fourier10-0100.png'); + bg10_1010 = p.loadImage(url_prefix + 'fourier10-0101.png'); + bg10_1001 = p.loadImage(url_prefix + 'fourier10-0110.png'); + bg10_1000 = p.loadImage(url_prefix + 'fourier10-0111.png'); + bg10_0111 = p.loadImage(url_prefix + 'fourier10-1000.png'); + bg10_0110 = p.loadImage(url_prefix + 'fourier10-1001.png'); + bg10_0101 = p.loadImage(url_prefix + 'fourier10-1010.png'); + bg10_0100 = p.loadImage(url_prefix + 'fourier10-1011.png'); + bg10_0011 = p.loadImage(url_prefix + 'fourier10-1100.png'); + bg10_0010 = p.loadImage(url_prefix + 'fourier10-1101.png'); + bg10_0001 = p.loadImage(url_prefix + 'fourier10-1110.png'); + bg10_0000 = p.loadImage(url_prefix + 'fourier10-1111.png'); + + } + + p.draw = function() { + str1 = mouse1.toString(); + str2 = mouse2.toString(); + str3 = mouse3.toString(); + str4 = mouse4.toString(); + picName = "bg10_" + str1 + str2 + str3 + str4; + p.background(eval(picName)); + } + + p.mouseClicked = function() { + if ((p.mouseX > 72) && (p.mouseX < 160) && (p.mouseY > 20) && (p.mouseY < 97)) { + mouse1 = (mouse1 + 1)%2; + } + if ((p.mouseX > 285) && (p.mouseX < 373) && (p.mouseY > 20) && (p.mouseY < 97)) { + mouse2 = (mouse2 + 1)%2; + } + if ((p.mouseX > 72) && (p.mouseX < 160) && (p.mouseY > 115) && (p.mouseY < 192)) { + mouse3 = (mouse3 + 1)%2; + } + if ((p.mouseX > 285) && (p.mouseX < 373) && (p.mouseY > 115) && (p.mouseY < 192)) { + mouse4 = (mouse4 + 1)%2; + } + } +} + +var myp510 = new p5(sketch10, 'sketch10'); +///// Sketch 11 + +var sketch11 = function(p) { + var mouse1 = 0; + var mod = 0.75; + var width = 600*mod, height = 400*mod; + + p.setup = function() { + const canvas1 = p.createCanvas(width, height); + + bg11_0 = p.loadImage(url_prefix + 'fourier11-0.png'); + bg11_1 = p.loadImage(url_prefix + 'fourier11-1.png'); + + } + + p.draw = function() { + str1 = mouse1.toString(); + picName = "bg11_" + str1; + p.background(eval(picName)); + } + + p.mouseClicked = function() { + if ((p.mouseX > 200) && (p.mouseX < 250) && (p.mouseY > 140) && (p.mouseY < 190)) { + mouse1 = (mouse1 + 1)%2; + } + } +} + +var myp511 = new p5(sketch11, 'sketch11'); + +///// Sketch 11_5 + +var sketch11_5 = function(p) { + var mouse1 = 0; + var mod = 0.75; + var width = 600*mod, height = 400*mod; + + p.setup = function() { + const canvas1 = p.createCanvas(width, height); + + bg11_2 = p.loadImage(url_prefix + 'fourier11-2.png'); + bg11_3 = p.loadImage(url_prefix + 'fourier11-3.png'); + + } + + p.draw = function() { + str1 = (mouse1+2).toString(); + picName = "bg11_" + str1; + p.background(eval(picName)); + } + + p.mouseClicked = function() { + if ((p.mouseX > 0) && (p.mouseX < width) && (p.mouseY > 0) && (p.mouseY < height)) { + mouse1 = (mouse1 + 1)%2; + } + } +} + +var myp5115 = new p5(sketch11_5, 'sketch11_5'); + +///// Sketch 12 + +var sketch12 = function(p) { + var mod = 0.75; + var width = 600*mod, height = 400*mod; + var theta = 0; + var rad = 57; + + p.setup = function() { + const canvas1 = p.createCanvas(width, height); + bg12 = p.loadImage(url_prefix + 'fourier12.png'); + label = p.loadImage(url_prefix + 'fourier12-diff.png'); + } + + p.draw = function() { + p.background(bg12); + + if (((p.mouseY < height) && (p.mouseY > 0)) && ((p.mouseX < width) && (p.mouseX > 0))) { + theta = 2*3.15*p.mouseX/width; + } + horz = p.cos(theta); + vert = p.sin(theta); + + p.stroke(255, 181, 241); + p.strokeWeight(2); + p.line(222 + rad*horz, 153 - rad*vert, 222, 153); + p.line(222 + rad*horz, 153 - rad*vert, 222, 153 - 2*rad*vert); + p.stroke(199, 86, 178); + p.line(222, 153, 222, 155 - 2*rad*vert); + + p.noStroke(); + p.fill(215, 162, 246); + p.circle(p.mouseX, 255, 10); + p.noFill(); + p.stroke(215, 162, 246); + p.arc(222, 153, 30, 30, -theta, 0); + + p.image(label, 222 - 20, 153 - 2*rad*vert - 40, label.width/3.5, label.height/3.5); + } +} + +var myp512 = new p5(sketch12, 'sketch12'); +///// Sketch 13 + +var sketch13 = function(p) { + var mod = 0.75; + var width = 600*mod, height = 400*mod; + var theta = 0; + var rad = 43; + + p.setup = function() { + const canvas1 = p.createCanvas(width, height); + bg13 = p.loadImage(url_prefix + 'fourier13.png'); + } + + p.draw = function() { + p.background(bg13); + + if (((p.mouseY < height) && (p.mouseY > 0)) && ((p.mouseX < width) && (p.mouseX > 0))) { + theta = 2*2*3.155*p.mouseX/width - 0.02; + } + horz = p.cos(theta); + vert = p.sin(theta); + horz2 = p.cos(2*theta); + vert2 = p.sin(2*theta); + horz3 = p.cos(3*theta); + vert3 = p.sin(3*theta); + + p.stroke(255, 181, 241); + p.strokeWeight(2); + p.line(225 + rad*horz, 90 - rad*vert, 225, 90); + p.line(225 + rad*horz, 90 - rad*vert, 225, 90 - 2*rad*vert); + p.stroke(199, 86, 178); + p.line(225, 90, 225, 90 - 2*rad*vert); + + if (((p.mouseY < height) && (p.mouseY > 0)) && ((p.mouseX < width) && (p.mouseX > 0))) { + p.noStroke(); + p.fill(215, 162, 246); + p.circle(p.mouseX, 255, 10); + } + p.noFill(); + p.stroke(215, 162, 246); + p.arc(225, 90, 30, 30, -theta, 0); + + p.stroke(255, 181, 241); + p.strokeWeight(2); + p.line(107 + rad*horz2, 162 - rad*vert2, 107, 162); + p.line(107 + rad*horz2, 162 - rad*vert2, 107, 162 - 2*rad*vert2); + p.stroke(199, 86, 178); + p.line(107, 162, 107, 162 - 2*rad*vert2); + + p.noFill(); + p.stroke(215, 162, 246); + p.arc(107, 162, 30, 30, -2*theta, 0); + + p.stroke(255, 181, 241); + p.strokeWeight(2); + p.line(331 + rad*horz3, 162 - rad*vert3, 331, 160); + p.line(331 + rad*horz3, 162 - rad*vert3, 331, 160 - 2*rad*vert3); + p.stroke(199, 86, 178); + p.line(331, 162, 331, 162 - 2*rad*vert3); + + p.noFill(); + p.stroke(215, 162, 246); + p.arc(331, 162, 30, 30, -3*theta, 0); + } +} + +var myp513 = new p5(sketch13, 'sketch13'); + +///// Sketch 14 + +var sketch14 = function(p) { + var mod = 0.75; + var width = 600*mod, height = 400*mod; + var theta = 0; + var rad = 43; + var angle1 = 0.02, angle2 = 0.13, angle3 = 0.4, angle4 = 0.63, angle5 = 0.7, angle6 = 0.95; + + p.setup = function() { + const canvas1 = p.createCanvas(width, height); + bg13 = p.loadImage(url_prefix + 'fourier13.png'); + } + + p.draw = function() { + p.background(bg13); + + if ((p.mouseY < height) && (p.mouseY > 0)) { + if ((p.mouseX > 0) && (p.mouseX < angle1*width)) { + theta = 2*2*3.155*angle1 - 0.02; + } + if ((p.mouseX > angle1*width) && (p.mouseX < angle2*width)) { + theta = 2*2*3.155*angle2 - 0.02; + } + if ((p.mouseX > angle2*width) && (p.mouseX < angle3*width)) { + theta = 2*2*3.155*angle3 - 0.02; + } + if ((p.mouseX > angle3*width) && (p.mouseX < angle4*width)) { + theta = 2*2*3.155*angle4 - 0.02; + } + if ((p.mouseX > angle4*width) && (p.mouseX < angle5*width)) { + theta = 2*2*3.155*angle5 - 0.02; + } + else if (p.mouseX > angle5*width) { + theta = 2*2*3.155*angle6 - 0.02; + } + } + horz = p.cos(theta); + vert = p.sin(theta); + horz2 = p.cos(2*theta); + vert2 = p.sin(2*theta); + horz3 = p.cos(3*theta); + vert3 = p.sin(3*theta); + + p.stroke(199, 86, 178); + p.strokeWeight(2); + p.line(225 + rad*horz, 90 - rad*vert, 225, 90); + + p.noStroke(); + p.fill(240, 238, 239); + p.circle(angle1*width, 255, 10); + p.circle(angle2*width, 255, 10); + p.circle(angle3*width, 255, 10); + p.circle(angle4*width, 255, 10); + p.circle(angle5*width, 255, 10); + p.circle(angle6*width, 255, 10); + + if (((p.mouseY < height) && (p.mouseY > 0)) && ((p.mouseX < width) && (p.mouseX > 0))) { + p.noStroke(); + p.fill(215, 162, 246); + p.circle(theta*width/(2*2*3.155), 255, 10); + } + + p.noFill(); + p.stroke(215, 162, 246); + p.arc(225, 90, 30, 30, -theta, 0); + + p.stroke(199, 86, 178); + p.strokeWeight(2); + p.line(107 + rad*horz2, 162 - rad*vert2, 107, 162); + + p.noFill(); + p.stroke(215, 162, 246); + p.arc(107, 162, 30, 30, -2*theta, 0); + + p.stroke(199, 86, 178); + p.strokeWeight(2); + p.line(331 + rad*horz3, 162 - rad*vert3, 331, 160); + + p.noFill(); + p.stroke(215, 162, 246); + p.arc(331, 162, 30, 30, -3*theta, 0); + } +} + +var myp514 = new p5(sketch14, 'sketch14'); + +///// Sketch 14_5 + +var sketch14_5 = function(p) { + var mod = 0.75; + var width = 600*mod, height = 400*mod; + var rad = 43; + var angle1 = 0.02, angle2 = 0.13, angle3 = 0.4, angle4 = 0.63, angle5 = 0.7, angle6 = 0.95; + var theta = 0, angle = angle1; + var xinit = 220, yinit = 153; + var x = xinit, y = yinit; + + p.setup = function() { + const canvas1 = p.createCanvas(width, height); + bg14_5 = p.loadImage(url_prefix + 'fourier14_5.png'); + } + + p.draw = function() { + p.background(bg14_5); + + if (((p.mouseY < height) && (p.mouseY > 0)) && ((p.mouseX < width) && (p.mouseX > 0))) { + theta = 2*2*3.142*p.mouseX/width; + } + + p.stroke(255, 181, 241) + p.strokeWeight(2); + + x = xinit; + y = yinit; + for (let i = -3; i < 4; i++) { + p.line(x, y, x + rad*p.cos(i*(theta-angle)), y - rad*p.sin(i*(theta-angle))); + x = x + rad*p.cos(i*(theta-angle)); + y = y - rad*p.sin(i*(theta-angle)); + } + p.stroke(199, 86, 178); + p.line(x, y, xinit, yinit); + + p.noStroke(); + p.fill(240, 238, 239); + p.circle(angle1*width, 255, 10); + p.circle(angle2*width, 255, 10); + p.circle(angle3*width, 255, 10); + p.circle(angle4*width, 255, 10); + p.circle(angle5*width, 255, 10); + p.circle(angle6*width, 255, 10); + + if (((p.mouseY < height) && (p.mouseY > 0)) && ((p.mouseX < width) && (p.mouseX > 0))) { + p.noStroke(); + p.fill(215, 162, 246); + p.circle(theta*width/(2*2*3.155), 255, 10); + } + + p.mouseClicked = function() { + if ((p.mouseX > 0) && (p.mouseX < width) && (p.mouseY > 0) && (p.mouseY < height)) { + if ((p.mouseY < height) && (p.mouseY > 0)) { + if ((p.mouseX > 0) && (p.mouseX < angle1*width)) { + angle = 2*2*3.142*angle1; + } + if ((p.mouseX > angle1*width) && (p.mouseX < angle2*width)) { + angle = 2*2*3.142*angle2; + } + if ((p.mouseX > angle2*width) && (p.mouseX < angle3*width)) { + angle = 2*2*3.142*angle3; + } + if ((p.mouseX > angle3*width) && (p.mouseX < angle4*width)) { + angle = 2*2*3.142*angle4; + } + if ((p.mouseX > angle4*width) && (p.mouseX < angle5*width)) { + angle = 2*2*3.142*angle5; + } + else if (p.mouseX > angle5*width) { + angle = 2*2*3.142*angle6; + } + } + } + } + p.noStroke(); + p.fill(215, 162, 246); + p.circle(angle*width/(2*2*3.155), 255, 10); + } +} + +var myp5 = new p5(sketch14_5, 'sketch14_5'); + +///// Sketch 15 + +var sketch15 = function(p) { + var mod = 0.75; + var width = 600*mod, height = 400*mod; + var rad = 43; + var angle1 = 1/7, angle2 = 2/7, angle3 = 3/7, angle4 = 4/7, angle5 = 5/7, angle6 = 6/7; + var theta = 0, angle = 2*2*3.142*angle1; + var xinit = 220, yinit = 153; + var x = xinit, y = yinit; + + p.setup = function() { + const canvas1 = p.createCanvas(width, height); + bg14_5 = p.loadImage(url_prefix + 'fourier14_5.png'); + } + + p.draw = function() { + p.background(bg14_5); + + if (((p.mouseY < height) && (p.mouseY > 0)) && ((p.mouseX < width) && (p.mouseX > 0))) { + theta = 2*2*3.14*p.mouseX/width; + } + + p.stroke(255, 181, 241) + p.strokeWeight(2); + + x = xinit; + y = yinit; + for (let i = -3; i < 4; i++) { + p.line(x, y, x + rad*p.cos(i*(theta-angle)), y - rad*p.sin(i*(theta-angle))); + x = x + rad*p.cos(i*(theta-angle)); + y = y - rad*p.sin(i*(theta-angle)); + } + p.stroke(199, 86, 178); + p.line(x, y, xinit, yinit); + + p.noStroke(); + p.fill(240, 238, 239); + p.circle(angle1*width, 255, 10); + p.circle(angle2*width, 255, 10); + p.circle(angle3*width, 255, 10); + p.circle(angle4*width, 255, 10); + p.circle(angle5*width, 255, 10); + p.circle(angle6*width, 255, 10); + + if (((p.mouseY < height) && (p.mouseY > 0)) && ((p.mouseX < width) && (p.mouseX > 0))) { + p.noStroke(); + p.fill(215, 162, 246); + p.circle(theta*width/(2*2*3.155), 255, 10); + } + + p.mouseClicked = function() { + if ((p.mouseX > 0) && (p.mouseX < width) && (p.mouseY > 0) && (p.mouseY < height)) { + if ((p.mouseY < height) && (p.mouseY > 0)) { + if ((p.mouseX > 0) && (p.mouseX < angle1*width)) { + angle = 2*2*3.142*angle1; + } + if ((p.mouseX > angle1*width) && (p.mouseX < angle2*width)) { + angle = 2*2*3.142*angle2; + } + if ((p.mouseX > angle2*width) && (p.mouseX < angle3*width)) { + angle = 2*2*3.142*angle3; + } + if ((p.mouseX > angle3*width) && (p.mouseX < angle4*width)) { + angle = 2*2*3.142*angle4; + } + if ((p.mouseX > angle4*width) && (p.mouseX < angle5*width)) { + angle = 2*2*3.142*angle5; + } + else if (p.mouseX > angle5*width) { + angle = 2*2*3.142*angle6; + } + } + } + } + p.noStroke(); + p.fill(215, 162, 246); + p.circle(angle*width/(2*2*3.155), 255, 10); + } +} + +var myp515 = new p5(sketch15, 'sketch15'); diff --git a/_static/demos/circuits_as_fourier_series/src/12-l4.png b/_static/demos/circuits_as_fourier_series/src/12-l4.png new file mode 100644 index 0000000000000000000000000000000000000000..11b95d7993b1b29dcd1d55b90384b8db41410ca4 GIT binary patch literal 13411 zcmW+-WmHsM6s1#2q&p-P7&@h6VCYgpy1S81si8qSB!&hl>29P$1nHFSuJ7^vn3=U^ zz4z|9=bn4+KKtw!p`s*4-XGED|;Ik6C+16HV0>mj3Z%EI5=uJu#~ul 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` From e9bbfb040c5437a0d93f258abddc66780a46d432 Mon Sep 17 00:00:00 2001 From: Guillermo Alonso-Linaje <65235481+KetpuntoG@users.noreply.github.com> Date: Mon, 11 Sep 2023 15:11:50 -0400 Subject: [PATCH 17/24] Update circuits_as_fourier_series.metadata.json (#921) fixing metadata And changing a path --- _static/demos/circuits_as_fourier_series/sketch.js | 2 +- demonstrations/circuits_as_fourier_series.metadata.json | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/_static/demos/circuits_as_fourier_series/sketch.js b/_static/demos/circuits_as_fourier_series/sketch.js index 8d339f93db..bf2b69f1d8 100644 --- a/_static/demos/circuits_as_fourier_series/sketch.js +++ b/_static/demos/circuits_as_fourier_series/sketch.js @@ -1,4 +1,4 @@ -var url_prefix = '../_static/demos/circuits_as_fourier_series/src/'; +var url_prefix = '/_static/demos/circuits_as_fourier_series/src/'; // fetch(url_prefix + 'fourier0-1.png', {method: 'HEAD'}).then(res => { // if (!res.ok) { diff --git a/demonstrations/circuits_as_fourier_series.metadata.json b/demonstrations/circuits_as_fourier_series.metadata.json index ae2e50858f..c9b2c2ed5b 100644 --- a/demonstrations/circuits_as_fourier_series.metadata.json +++ b/demonstrations/circuits_as_fourier_series.metadata.json @@ -15,7 +15,7 @@ "previewImages": [ { "type": "thumbnail", - "uri": "/demonstrations/thumbnail_circuits_as_fourier_series.png" + "uri": "/_images/thumbnail_circuits_as_fourier_series.png" }, { "type": "large_thumbnail", From c75357720b2e24d8eb12283e85fee14e8584614a Mon Sep 17 00:00:00 2001 From: Guillermo Alonso-Linaje <65235481+KetpuntoG@users.noreply.github.com> Date: Tue, 12 Sep 2023 13:39:15 -0400 Subject: [PATCH 18/24] Update sketch.js (#922) fixing issues in the Fourier circuit demo --------- Co-authored-by: Ashish Kanwar Singh <104938869+ashishks0522@users.noreply.github.com> --- _static/demos/circuits_as_fourier_series/sketch.js | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/_static/demos/circuits_as_fourier_series/sketch.js b/_static/demos/circuits_as_fourier_series/sketch.js index bf2b69f1d8..d019008577 100644 --- a/_static/demos/circuits_as_fourier_series/sketch.js +++ b/_static/demos/circuits_as_fourier_series/sketch.js @@ -1,10 +1,10 @@ -var url_prefix = '/_static/demos/circuits_as_fourier_series/src/'; +var url_prefix = '../../_static/demos/circuits_as_fourier_series/src/'; -// fetch(url_prefix + 'fourier0-1.png', {method: 'HEAD'}).then(res => { -// if (!res.ok) { -// url_prefix = '../_static/demos/circuits_as_fourier_series/src/'; -// } -// }); + fetch(url_prefix + 'fourier0-1.png', {method: 'HEAD'}).then(res => { + if (!res.ok) { + url_prefix = '../_static/demos/circuits_as_fourier_series/src/'; + } + }); ///// Sketch 0 From 0cb3257a058131aabb3ed2ccc74e4c6edaad3a5b Mon Sep 17 00:00:00 2001 From: Michael Vu Date: Wed, 13 Sep 2023 09:40:05 -0400 Subject: [PATCH 19/24] Add `text` Makefile commands (#924) **Title:** Add `text` Makefile commands **Summary:** Adds new commands: `make text` and `make text-norun` **Relevant references:** N/A **Possible Drawbacks:** There are many warning upon generation, but it works for our purposes **Related GitHub Issues:** N/A --- Makefile | 10 ++++++++++ 1 file changed, 10 insertions(+) diff --git a/Makefile b/Makefile index 08468a5a05..9e83f22352 100644 --- a/Makefile +++ b/Makefile @@ -45,6 +45,16 @@ json-norun: @echo @echo "Build finished. The JSON files are in $(BUILDDIR)." +text: + $(SPHINXBUILD) -b text "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) + @echo + @echo "Build finished. The text files are in $(BUILDDIR)." + +text-norun: + $(SPHINXBUILD) -D plot_gallery=0 -b text "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) + @echo + @echo "Build finished. The text files are in $(BUILDDIR)." + download: # make data directories mkdir -p $(DATADIR) From 8b8c75c07fefb8fb3636db8ce17cc013298f2f58 Mon Sep 17 00:00:00 2001 From: Guillermo Alonso-Linaje <65235481+KetpuntoG@users.noreply.github.com> Date: Thu, 14 Sep 2023 11:55:05 -0400 Subject: [PATCH 20/24] Update tutorial_learning2learn.py (#923) Tensorflow issue appeared in learning2learn demo. The problem has not been solved but it has been decided to revert to non-executable so as not to lock the repository. --- ...data.json => learning2learn.metadata.json} | 2 +- ...al_learning2learn.py => learning2learn.py} | 217 ++++++++++++++++++ 2 files changed, 218 insertions(+), 1 deletion(-) rename demonstrations/{tutorial_learning2learn.metadata.json => learning2learn.metadata.json} (94%) rename demonstrations/{tutorial_learning2learn.py => learning2learn.py} (80%) diff --git a/demonstrations/tutorial_learning2learn.metadata.json b/demonstrations/learning2learn.metadata.json similarity index 94% rename from demonstrations/tutorial_learning2learn.metadata.json rename to demonstrations/learning2learn.metadata.json index dd72b26938..6722ae44ca 100644 --- a/demonstrations/tutorial_learning2learn.metadata.json +++ b/demonstrations/learning2learn.metadata.json @@ -19,7 +19,7 @@ ], "seoDescription": "Use a classical recurrent neural network to initilize the parameters of a variational quatum algorithm.", "doi": "", - "canonicalURL": "/qml/demos/tutorial_learning2learn", + "canonicalURL": "/qml/demos/learning2learn", "references": [], "basedOnPapers": [], "referencedByPapers": [], diff --git a/demonstrations/tutorial_learning2learn.py b/demonstrations/learning2learn.py similarity index 80% rename from demonstrations/tutorial_learning2learn.py rename to demonstrations/learning2learn.py index 5bb97a1e61..52a5f9aa79 100644 --- a/demonstrations/tutorial_learning2learn.py +++ b/demonstrations/learning2learn.py @@ -119,6 +119,9 @@ Check out this great tutorial on how to use QAOA for solving graph problems: https://pennylane.ai/qml/demos/tutorial_qaoa_intro.html +.. note:: + Running the tutorial (excluding the Appendix) requires approx. ~13m. + """ ###################################################################### @@ -191,6 +194,12 @@ def generate_graphs(n_graphs, n_nodes, p_edge): nx.draw(graphs[0]) +###################################################################### +# .. figure:: ../demonstrations/learning2learn/rendered_Graph0.png +# :align: center +# :width: 70% +# :target: javascript:void(0); +# ###################################################################### # Variational Quantum Circuit: QAOA @@ -253,6 +262,15 @@ def hamiltonian(params, **kwargs): # Evaluate th QAOA instance just created with some angles. print(cost(x)) +############################################################################## +# .. rst-class:: sphx-glr-script-out +# +# Out: +# +# .. code-block:: none +# +# tf.Tensor(-3.193267957255582, shape=(), dtype=float64) +# ###################################################################### @@ -458,6 +476,46 @@ def train_step(graph_cost): print(f" > Graph {i+1}/{len(graph_cost_list)} - Loss: {loss[0][0]}") print(f" >> Mean Loss during epoch: {np.mean(total_loss)}") +############################################################################## +# .. rst-class:: sphx-glr-script-out +# +# Out: +# +# .. code-block:: none +# +# Epoch 1 +# > Graph 1/20 - Loss: -1.6641689538955688 +# > Graph 6/20 - Loss: -1.4186843633651733 +# > Graph 11/20 - Loss: -1.3757232427597046 +# > Graph 16/20 - Loss: -1.294339656829834 +# >> Mean Loss during epoch: -1.7352586269378663 +# Epoch 2 +# > Graph 1/20 - Loss: -2.119091749191284 +# > Graph 6/20 - Loss: -1.4789190292358398 +# > Graph 11/20 - Loss: -1.3779840469360352 +# > Graph 16/20 - Loss: -1.2963457107543945 +# >> Mean Loss during epoch: -1.8252217948436738 +# Epoch 3 +# > Graph 1/20 - Loss: -2.1322619915008545 +# > Graph 6/20 - Loss: -1.459418535232544 +# > Graph 11/20 - Loss: -1.390620470046997 +# > Graph 16/20 - Loss: -1.3165746927261353 +# >> Mean Loss during epoch: -1.8328069806098939 +# Epoch 4 +# > Graph 1/20 - Loss: -2.1432175636291504 +# > Graph 6/20 - Loss: -1.476362943649292 +# > Graph 11/20 - Loss: -1.3938289880752563 +# > Graph 16/20 - Loss: -1.3140206336975098 +# >> Mean Loss during epoch: -1.8369774043560028 +# Epoch 5 +# > Graph 1/20 - Loss: -2.1429405212402344 +# > Graph 6/20 - Loss: -1.477513074874878 +# > Graph 11/20 - Loss: -1.3909202814102173 +# > Graph 16/20 - Loss: -1.315887689590454 +# >> Mean Loss during epoch: -1.8371947884559632 +# + + ###################################################################### # As you can see, the Loss for each graph keeps decreasing across epochs, # indicating that the training routine is working correctly. @@ -479,6 +537,14 @@ def train_step(graph_cost): nx.draw(new_graph) +###################################################################### +# .. figure:: ../demonstrations/learning2learn/rendered_Graph1.png +# :align: center +# :width: 70% +# :target: javascript:void(0); +# + + ###################################################################### # Then we apply the trained RNN to this new graph, saving intermediate # results coming from all the recurrent iterations in the network. @@ -523,6 +589,11 @@ def train_step(graph_cost): plt.show() ###################################################################### +# .. figure:: ../demonstrations/learning2learn/rendered_LossLSTM.png +# :align: center +# :width: 70% +# :target: javascript:void(0); +# # That’s remarkable! The RNN learned to propose new parameters such that # the MaxCut cost is minimized very rapidly: in just a few iterations the # loss reaches a minimum. Actually, it takes just a single step for the LSTM @@ -569,6 +640,30 @@ def train_step(graph_cost): print(f"Final cost function: {new_cost(x).numpy()}\nOptimized angles: {x.numpy()}") ############################################################################## +# .. rst-class:: sphx-glr-script-out +# +# Out: +# +# .. code-block:: none +# +# Step 1 - Loss = -4.1700805 +# Step 2 - Loss = -4.67503588 +# Step 3 - Loss = -5.09949909 +# Step 4 - Loss = -5.40388533 +# Step 5 - Loss = -5.59529203 +# Step 6 - Loss = -5.70495197 +# Step 7 - Loss = -5.7642561 +# Step 8 - Loss = -5.79533198 +# Step 9 - Loss = -5.81138752 +# Step 10 - Loss = -5.81966529 +# Step 11 - Loss = -5.82396722 +# Step 12 - Loss = -5.82624537 +# Step 13 - Loss = -5.82749126 +# Step 14 - Loss = -5.82820626 +# Step 15 - Loss = -5.82864379 +# Final cost function: -5.828932361904984 +# Optimized angles: [[ 0.5865477 ] +# [-0.3228858]] # fig, ax = plt.subplots() @@ -584,6 +679,13 @@ def train_step(graph_cost): ax.set_xticks([0, 5, 10, 15, 20]); plt.show() +###################################################################### +# .. figure:: ../demonstrations/learning2learn/rendered_LossConfrontation.png +# :align: center +# :width: 70% +# :target: javascript:void(0); +# + ###################################################################### # *Hurray!* 🎉🎉 @@ -750,6 +852,30 @@ def train_step(graph_cost): print(f" > Graph {i+1}/{len(gs_cost_list)} - Loss: {loss}") print(f" >> Mean Loss during epoch: {np.mean(total_loss)}") +############################################################################## +# .. rst-class:: sphx-glr-script-out +# +# Out: +# +# .. code-block:: none +# +# Epoch 1 +# > Graph 1/15 - Loss: [[-1.4876363]] +# > Graph 6/15 - Loss: [[-1.8590403]] +# > Graph 11/15 - Loss: [[-1.7644017]] +# >> Mean Loss during epoch: -1.9704322338104248 +# Epoch 2 +# > Graph 1/15 - Loss: [[-1.8650053]] +# > Graph 6/15 - Loss: [[-1.9578737]] +# > Graph 11/15 - Loss: [[-1.8377447]] +# >> Mean Loss during epoch: -2.092947308222453 +# Epoch 3 +# > Graph 1/15 - Loss: [[-1.9009062]] +# > Graph 6/15 - Loss: [[-1.9726204]] +# > Graph 11/15 - Loss: [[-1.8668792]] +# >> Mean Loss during epoch: -2.1162660201390584 +# + ###################################################################### # Let’s check if this hybrid model eventually learned a good heuristic to @@ -766,6 +892,14 @@ def train_step(graph_cost): nx.draw(new_graph) +###################################################################### +# .. figure:: ../demonstrations/learning2learn/rendered_Graph10.png +# :align: center +# :width: 70% +# :target: javascript:void(0); +# + + ###################################################################### # We call the trained recurrent LSTM on this graph, saving not only the # last, but all intermediate guesses for the parameters. @@ -799,6 +933,14 @@ def train_step(graph_cost): ax.set_xticks([0, 5, 10, 15, 20]); plt.show() +###################################################################### +# .. figure:: ../demonstrations/learning2learn/rendered_LossGeneralization.png +# :align: center +# :width: 70% +# :target: javascript:void(0); +# + + ###################################################################### # Again, we can confirm that the custom optimizer based on the LSTM quickly reaches a good # value of the loss function, and also achieve good generalization performances, since @@ -846,6 +988,13 @@ def train_step(graph_cost): plt.show() ###################################################################### +# .. figure:: ../demonstrations/learning2learn/rendered_LossLandscape.png +# :align: center +# :width: 70% +# :target: javascript:void(0); +# +# +# # # Ideas for creating a Keras Layer and Keras Model # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ @@ -916,6 +1065,54 @@ def call(self, inputs): model.summary() + +############################################################################## +# .. rst-class:: sphx-glr-script-out +# +# Out: +# +# .. code-block:: none +# +# Model: "functional_1" +# __________________________________________________________________________________________________ +# Layer (type) Output Shape Param # Connected to +# ================================================================================================== +# input_1 (InputLayer) [(None, 1)] 0 +# __________________________________________________________________________________________________ +# input_2 (InputLayer) [(None, 2)] 0 +# __________________________________________________________________________________________________ +# input_3 (InputLayer) [(None, 2)] 0 +# __________________________________________________________________________________________________ +# input_4 (InputLayer) [(None, 2)] 0 +# __________________________________________________________________________________________________ +# qrnn (QRNN) [(1, 1), 48 input_1[0][0] +# (None, 2), input_2[0][0] +# (None, 2), input_3[0][0] +# (None, 2)] input_4[0][0] +# qrnn[0][0] +# qrnn[0][1] +# qrnn[0][2] +# qrnn[0][3] +# qrnn[1][0] +# qrnn[1][1] +# qrnn[1][2] +# qrnn[1][3] +# __________________________________________________________________________________________________ +# tf.math.multiply (TFOpLambda) (1, 1) 0 qrnn[0][0] +# __________________________________________________________________________________________________ +# tf.math.multiply_1 (TFOpLambda) (1, 1) 0 qrnn[1][0] +# __________________________________________________________________________________________________ +# tf.math.multiply_2 (TFOpLambda) (1, 1) 0 qrnn[2][0] +# __________________________________________________________________________________________________ +# average_147 (Average) (1, 1) 0 tf.math.multiply[0][0] +# tf.math.multiply_1[0][0] +# tf.math.multiply_2[0][0] +# ================================================================================================== +# Total params: 48 +# Trainable params: 48 +# Non-trainable params: 0 +# + ###################################################################### # A basic training routine for the ``Keras Model`` just created: # @@ -948,6 +1145,26 @@ def call(self, inputs): for t, s in zip(pred, ["out0", "out1", "out2", "Loss"]): print(f" >{s}: {t.numpy()}") +############################################################################## +# .. rst-class:: sphx-glr-script-out +# +# Out: +# +# .. code-block:: none +# +# Step 1 - Loss = [[-1.5563084]] - Cost = -4.762684301954701 +# Step 2 - Loss = [[-1.5649065]] - Cost = -4.799981173473755 +# Step 3 - Loss = [[-1.5741502]] - Cost = -4.840036354736862 +# Step 4 - Loss = [[-1.5841404]] - Cost = -4.883246647056216 +# Step 5 - Loss = [[-1.5948243]] - Cost = -4.929228976649736 +# Final Loss: [[-1.5948243]] +# Final Outs: +# >out0: [[-0.01041588 0.01016874]] +# >out1: [[-0.04530389 0.38148248]] +# >out2: [[-0.10258182 0.4134117 ]] +# >Loss: [[-1.5948243]] +# + ###################################################################### # .. note:: # This code works only for a single graph at a time, since a graph was From fc28b4f28e8260022c5132e73035229e6999bde6 Mon Sep 17 00:00:00 2001 From: Ashish Kanwar Singh <104938869+ashishks0522@users.noreply.github.com> Date: Thu, 14 Sep 2023 16:23:33 -0400 Subject: [PATCH 21/24] deploy-to-prod-fix (#928) --- .github/workflows/deploy-demos-to-prod.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/deploy-demos-to-prod.yml b/.github/workflows/deploy-demos-to-prod.yml index ba81e793db..747ffc7b59 100644 --- a/.github/workflows/deploy-demos-to-prod.yml +++ b/.github/workflows/deploy-demos-to-prod.yml @@ -17,7 +17,7 @@ jobs: AWS_S3_BUCKET_ID: ${{ secrets.PL_SITE_STAGING_QML_JSON_S3_BUCKET_ID }} AWS_S3_HTML_BUCKET_ID: ${{ secrets.PL_SITE_STAGING_S3_BUCKET_NAME }} run: | - aws s3 cp s3://$AWS_S3_BUCKET_ID/master ./local-master + aws s3 cp --recursive s3://$AWS_S3_BUCKET_ID/master ./local-master aws s3 cp s3://$AWS_S3_HTML_BUCKET_ID/qml/searchindex.js searchindex.js - name: Push production bucket From 5d617d4452687d067d6bd7181de0b1d5a68df521 Mon Sep 17 00:00:00 2001 From: Rashid N H M <95639609+rashidnhm@users.noreply.github.com> Date: Fri, 15 Sep 2023 10:23:01 -0400 Subject: [PATCH 22/24] Add metadata previewImages validation (#887) --- .github/workflows/build-branch.yml | 45 ++++-- .github/workflows/build-pr.yml | 41 ++++- .github/workflows/deploy-pr.yml | 2 +- .../qml_pipeline_utils/pyproject.toml | 14 ++ .../qml_pipeline_utils/cli.py | 22 +++ .../qml_pipeline_utils/services/__init__.py | 1 + .../validate_metadata_preview_images.py | 41 +++++ .github/workflows/qml_pipeline_utils/setup.py | 14 -- .github/workflows/teardown-pr.yml | 2 +- .github/workflows/validate-demo-metadata.yml | 150 +++++++++++++++--- .gitignore | 5 + poetry.lock | 8 +- 12 files changed, 291 insertions(+), 54 deletions(-) create mode 100644 .github/workflows/qml_pipeline_utils/pyproject.toml create mode 100644 .github/workflows/qml_pipeline_utils/qml_pipeline_utils/services/validate_metadata_preview_images.py delete mode 100644 .github/workflows/qml_pipeline_utils/setup.py diff --git a/.github/workflows/build-branch.yml b/.github/workflows/build-branch.yml index c71df7c2cf..397795efda 100644 --- a/.github/workflows/build-branch.yml +++ b/.github/workflows/build-branch.yml @@ -54,9 +54,17 @@ on: type: boolean default: false + # TODO: This is temp workaround to add metadata previewImages validation to this job. Once the static asset files + # are centralized properly, then this parameter can be removed. + validate_metadata_preview_images: + description: Validate the metadata previewImages after sphinx-build concludes + required: false + type: boolean + default: false + jobs: validate-inputs: - runs-on: ubuntu-22.04 + runs-on: ubuntu-latest steps: - name: Validate inputs.sphinx_build_output_format run: | @@ -64,7 +72,7 @@ jobs: [[ " $valid_choices " =~ " ${{ inputs.sphinx_build_output_format }} " ]] && exit 0 || exit 1 compute-build-strategy-matrix: - runs-on: ubuntu-22.04 + runs-on: ubuntu-latest steps: - name: Checkout uses: actions/checkout@v3 @@ -78,9 +86,7 @@ jobs: python-version: 3.9 - name: Install QML Pipeline Utils - run: | - cd .github/workflows/qml_pipeline_utils - pip install . + run: pip install .github/workflows/qml_pipeline_utils - name: Execution Times Cache id: execution_times_cache @@ -217,7 +223,7 @@ jobs: worker-load-artifact-name: ${{ steps.compute-strategy-matrix.outputs.worker_load_artifact_name }} validate-poetry-lock-file: - runs-on: ubuntu-22.04 + runs-on: ubuntu-latest steps: - uses: actions/checkout@v3 @@ -244,7 +250,7 @@ jobs: run: ${{ steps.poetry.outputs.bin }} lock --check build-branch: - runs-on: ubuntu-22.04 + runs-on: ubuntu-latest needs: - compute-build-strategy-matrix - validate-inputs @@ -279,9 +285,7 @@ jobs: echo "${{ github.workspace }}/venv/bin" >> $GITHUB_PATH - name: Install QML Pipeline Utils - run: | - cd .github/workflows/qml_pipeline_utils - ${{ steps.venv.outputs.location }}/bin/python3 -m pip install . + run: ${{ steps.venv.outputs.location }}/bin/python3 -m pip install .github/workflows/qml_pipeline_utils - name: Install Poetry id: poetry @@ -427,6 +431,25 @@ jobs: cat /tmp/execution_times/execution_times.json | jq + # TODO: This is a temporary step to include metadata validation as a part of the build step. + # One the assets in QML are centralized with proper naming convention, this step can be removed + # and the validation can be done with `validate-demo-metadata.yml` workflow. + - name: Validate metadata previewImages + run: | + metadata_files=() + input_demos_to_build=$(jq -r '[.[].name]|join(" ")' ${{ steps.worker_tasks.outputs.file_name }}) + + for metadata_file in $input_demos_to_build + do + metadata_files+=("demonstrations/${metadata_file%.py}.metadata.json") + done + + metadata_files_str="${metadata_files[@]}" + + qml_pipeline_utils validate-metadata-preview-images \ + --metadata-files $metadata_files_str \ + --build-dir="${{ github.workspace }}/_build/html" + - name: Upload Execution Times uses: actions/upload-artifact@v3 with: @@ -490,7 +513,7 @@ jobs: !_build/html/glossary aggregate_build: - runs-on: ubuntu-22.04 + runs-on: ubuntu-latest if: inputs.skip_execution_times_aggregation == false || inputs.skip_sphinx_build_file_aggregation == false needs: - build-branch diff --git a/.github/workflows/build-pr.yml b/.github/workflows/build-pr.yml index 6eed0ae252..46a9a0c935 100644 --- a/.github/workflows/build-pr.yml +++ b/.github/workflows/build-pr.yml @@ -65,6 +65,30 @@ jobs: done echo "files=${demo_filenames[@]}" >> $GITHUB_OUTPUT + - name: Detect Metadata Files that need to be changed + id: metadata_files_to_check + env: + DEMOS_TO_BUILD: ${{ steps.formatted_changed_demos.outputs.files }} + run: | + metadata_files=() + input_demos_to_build="$DEMOS_TO_BUILD" + + if [ -z "$input_demos_to_build" ]; then + readarray -d '' metadata_files < <(find demonstrations -name "*.metadata.json" -not -path "demonstrations/demonstrations_categories.metadata.json" -print0) + else + for metadata_file in $input_demos_to_build + do + metadata_files+=("demonstrations/${metadata_file%.py}.metadata.json") + done + fi + + metadata_files_str="${metadata_files[@]}" + + echo 'The following metadata files will be validated =>' + echo "$metadata_files_str" + + echo "file_names=$metadata_files_str" >> $GITHUB_OUTPUT + - name: Set number of workers needed id: num_workers env: @@ -81,12 +105,25 @@ jobs: num_workers: ${{ steps.num_workers.outputs.num_workers }} build_all_demos: ${{ steps.build_all_demos.outputs.build_all_demos }} demos_to_build: ${{ steps.formatted_changed_demos.outputs.files }} + metadata_file_names: ${{ steps.metadata_files_to_check.outputs.file_names }} + + validate_metadata: + uses: ./.github/workflows/validate-demo-metadata.yml + + needs: + - set_build_params + + with: + branch: ${{ github.ref }} + metadata_files: ${{ needs.set_build_params.outputs.metadata_file_names }} + skip_metadata_preview_image_value_validation: true # TODO: This can be reverted once the static assets are centralized build: uses: ./.github/workflows/build-branch.yml needs: - set_build_params + - validate_metadata # Only run if we need to build all the demos or a subset of demos. # If build_all_demos is false AND demos_to_build_is blank, this means @@ -111,12 +148,12 @@ jobs: sphinx_build_output_format: html sphinx_examples_to_build: ${{ needs.set_build_params.outputs.demos_to_build }} - save-build-context: - runs-on: ubuntu-22.04 + runs-on: ubuntu-latest needs: - build + - validate_metadata steps: - name: Save Pull Request Event Context diff --git a/.github/workflows/deploy-pr.yml b/.github/workflows/deploy-pr.yml index 9c93298a78..91c64e3b42 100644 --- a/.github/workflows/deploy-pr.yml +++ b/.github/workflows/deploy-pr.yml @@ -8,7 +8,7 @@ on: jobs: deploy: - runs-on: ubuntu-20.04 + runs-on: ubuntu-latest if: github.event.workflow_run.conclusion == 'success' steps: - name: Set Build Context File Prefix diff --git a/.github/workflows/qml_pipeline_utils/pyproject.toml b/.github/workflows/qml_pipeline_utils/pyproject.toml new file mode 100644 index 0000000000..98aeba7d6d --- /dev/null +++ b/.github/workflows/qml_pipeline_utils/pyproject.toml @@ -0,0 +1,14 @@ +[build-system] +requires = ["setuptools"] +build-backend = "setuptools.build_meta" + +[project] +name = "qml_pipeline_utils" +version = "1" +requires-python = ">= 3.9" +dependencies = [ + 'tqdm~=4.66.0' +] + +[project.scripts] +qml_pipeline_utils = "qml_pipeline_utils.cli:cli_parser" diff --git a/.github/workflows/qml_pipeline_utils/qml_pipeline_utils/cli.py b/.github/workflows/qml_pipeline_utils/qml_pipeline_utils/cli.py index c5cfde2db0..4d2e32e761 100644 --- a/.github/workflows/qml_pipeline_utils/qml_pipeline_utils/cli.py +++ b/.github/workflows/qml_pipeline_utils/qml_pipeline_utils/cli.py @@ -1,5 +1,6 @@ import json import argparse +import os from pathlib import Path import qml_pipeline_utils.services @@ -153,6 +154,20 @@ def cli_parser(): "glob-pattern", ) + subparsers_validate_metadata_preview_images = subparsers.add_parser( + "validate-metadata-preview-images", + description="Validate the metadata" + ) + add_flags_to_subparser( + subparsers_validate_metadata_preview_images, + "build-dir" + ) + subparsers_validate_metadata_preview_images.add_argument( + "--metadata-files", + help="List of paths to metadata files", + nargs="+" + ) + parser_results = parser.parse_args() cli_actions = { @@ -225,6 +240,13 @@ def cli_parser(): "sphinx_build_type": getattr(parser_results, "build_type", ""), }, }, + "validate-metadata-preview-images": { + "func": qml_pipeline_utils.services.validate_metadata_preview_images, + "kwargs": { + "metadata_files": getattr(parser_results, "metadata_files", []), + "sphinx_build_directory": Path(getattr(parser_results, "build_dir", "")) + } + } } if parser_results.action not in cli_actions: diff --git a/.github/workflows/qml_pipeline_utils/qml_pipeline_utils/services/__init__.py b/.github/workflows/qml_pipeline_utils/qml_pipeline_utils/services/__init__.py index 78e4ba1728..5f3588caa2 100644 --- a/.github/workflows/qml_pipeline_utils/qml_pipeline_utils/services/__init__.py +++ b/.github/workflows/qml_pipeline_utils/qml_pipeline_utils/services/__init__.py @@ -6,3 +6,4 @@ from .clean_sitemap import clean_sitemap from .show_worker_files import show_worker_files from .parse_execution_times import parse_execution_times +from .validate_metadata_preview_images import validate_metadata_preview_images diff --git a/.github/workflows/qml_pipeline_utils/qml_pipeline_utils/services/validate_metadata_preview_images.py b/.github/workflows/qml_pipeline_utils/qml_pipeline_utils/services/validate_metadata_preview_images.py new file mode 100644 index 0000000000..ab0f6021e2 --- /dev/null +++ b/.github/workflows/qml_pipeline_utils/qml_pipeline_utils/services/validate_metadata_preview_images.py @@ -0,0 +1,41 @@ +import os +import json +from pathlib import Path +from typing import List + +from tqdm import tqdm + + +def validate_metadata_preview_images(metadata_files: List[str], sphinx_build_directory: Path): + working_dir = Path(os.getcwd()) + + if sphinx_build_directory.is_absolute(): + sphinx_build_directory = sphinx_build_directory + else: + sphinx_build_directory = working_dir / sphinx_build_directory + + for metadata_file in metadata_files: + if not metadata_file.startswith("/"): + metadata_file_path = Path(working_dir) / metadata_file + else: + metadata_file_path = Path(metadata_file) + + with metadata_file_path.open() as fh: + metadata = json.load(fh) + + preview_images = metadata.get("previewImages", []) + + image_uris = [ + image["uri"] + for image in preview_images + ] + + for image_uri in tqdm(image_uris, ascii=True, desc=metadata_file): + if not image_uri.startswith("/"): + print(f" Metadata file {metadata_file} contains either remote image uri or non-absolute path " + f"for previewImage {image_uri}. Remote image paths cannot be validated at this time. Skipping.") + continue + + uri_path = sphinx_build_directory / image_uri[1:] + + assert uri_path.exists(), f"Could not find previewImage {image_uri}" diff --git a/.github/workflows/qml_pipeline_utils/setup.py b/.github/workflows/qml_pipeline_utils/setup.py deleted file mode 100644 index 1c84341221..0000000000 --- a/.github/workflows/qml_pipeline_utils/setup.py +++ /dev/null @@ -1,14 +0,0 @@ -#!/usr/bin/env python3 - -try: - from setuptools import setup -except ImportError: - from distutils.core import setup - -setup( - name="qml_pipeline_utils", - version="1", - url="https://github.com/PennyLaneAI/qml", - packages=["qml_pipeline_utils", "qml_pipeline_utils.services"], - entry_points={"console_scripts": ["qml_pipeline_utils=qml_pipeline_utils.cli:cli_parser"]}, -) diff --git a/.github/workflows/teardown-pr.yml b/.github/workflows/teardown-pr.yml index 3e2faa1156..7e0390e6d1 100644 --- a/.github/workflows/teardown-pr.yml +++ b/.github/workflows/teardown-pr.yml @@ -11,7 +11,7 @@ env: jobs: teardown: - runs-on: ubuntu-20.04 + runs-on: ubuntu-latest steps: - name: Check if deployment exists id: check_deployment diff --git a/.github/workflows/validate-demo-metadata.yml b/.github/workflows/validate-demo-metadata.yml index 671b779b2e..4a76e566fa 100644 --- a/.github/workflows/validate-demo-metadata.yml +++ b/.github/workflows/validate-demo-metadata.yml @@ -1,37 +1,149 @@ -# Starting as a separate Action as a POC. -# Consider combining this with the build-pr -# Action if we want to keep it. name: Validate Demo Metadata on: - pull_request: - push: - branches: - - master - - dev -concurrency: - group: metadata-validation-${{ github.ref }} - cancel-in-progress: true + workflow_call: + inputs: + branch: + description: The GitHub branch to checkout and validate metadata for + required: true + type: string + metadata_files: + description: | + List of metadata files to validate, separated by space. + Not passing a value for this input is the equivalent of validating all the metadata files. + required: false + type: string + default: '' + skip_metadata_schema_validation: + description: Skip validation of metadata schema validation + required: false + type: boolean + default: false + skip_metadata_preview_image_value_validation: + description: Skip validation of previewImages in metadata + required: false + type: boolean + default: false + jobs: - validate-demos: - runs-on: ubuntu-22.04 + generate-metadata-file-list: + runs-on: ubuntu-latest + + steps: + - name: Checkout + uses: actions/checkout@v3 + with: + fetch-depth: 1 + ref: ${{ inputs.branch }} + + - name: Get metadata file names + id: metadata_files + if: inputs.metadata_files == '' + run: | + file_names=$(find demonstrations -name "*.metadata.json" -not -path "*/demonstrations_categories.metadata.json" | tr '\n' ' ') + echo "file_names=$file_names" >> $GITHUB_OUTPUT + + - name: Format metadata file names + id: format_metadata_files + run: | + input_metadata_file_names="${{ inputs.metadata_files || steps.metadata_files.outputs.file_names }}" + formatted_metadata_file_names=() + + for metadata_file_name in $input_metadata_file_names + do + if [[ "$metadata_file_name" =~ "../demonstrations" ]]; then + formatted_metadata_file_names+=("$metadata_file_name") + elif [[ "$metadata_file_name" =~ "demonstrations" ]]; then + formatted_metadata_file_names+=("../$metadata_file_name") + else + formatted_metadata_file_names+=("../demonstrations/$metadata_file_name") + fi + done + + echo "formatted_file_names=${formatted_metadata_file_names[@]}" >> $GITHUB_OUTPUT + + outputs: + metadata_files: ${{ steps.format_metadata_files.outputs.formatted_file_names }} + + validate-metadata-schema: + runs-on: ubuntu-latest + + if: inputs.skip_metadata_schema_validation == false + + needs: + - generate-metadata-file-list + steps: - name: Checkout uses: actions/checkout@v3 with: fetch-depth: 1 + ref: ${{ inputs.branch }} - name: Setup Python uses: actions/setup-python@v4 with: python-version: 3.9 - - name: Install Python Dependencies + - name: Install Poetry + id: poetry + env: + POETRY_HOME: /tmp/poetry run: | - pip install poetry - poetry install --only metadata-validation + curl -sSL https://install.python-poetry.org -o install-poetry.py + python3 install-poetry.py --version 1.5.1 + echo "bin=${{ env.POETRY_HOME }}/bin/poetry" >> $GITHUB_OUTPUT - - name: Validate Metadata + - name: Install Metadata Schema Validation Dependencies + run: ${{ steps.poetry.outputs.bin }} install --only metadata-validation + + - name: Validate Metadata Schema + env: + METADATA_FILE_LIST: ${{ needs.generate-metadata-file-list.outputs.metadata_files }} run: | cd metadata_schemas - files=`find ../demonstrations -name "*.metadata.json" -not -path "*/demonstrations_categories.metadata.json" | tr '\n' ' '` - poetry run check-jsonschema -v --traceback-mode full --schemafile demo.metadata.schema.0.1.0.json $(echo $files) + ${{ steps.poetry.outputs.bin }} run check-jsonschema -v --traceback-mode full --schemafile demo.metadata.schema.0.1.0.json $METADATA_FILE_LIST + + validate-metadata-preview-images: + runs-on: ubuntu-latest + + if: inputs.skip_metadata_preview_image_value_validation == false + + needs: + - generate-metadata-file-list + + steps: + - name: Checkout + uses: actions/checkout@v3 + with: + fetch-depth: 1 + ref: ${{ inputs.branch }} + + - name: Setup Python + uses: actions/setup-python@v4 + with: + python-version: 3.9 + + - name: Install QML Pipeline Utils + run: pip install .github/workflows/qml_pipeline_utils + + - name: Re-Format metadata file-list + id: reformatted-metadata-file-list + run: | + metadata_file_names="${{ needs.generate-metadata-file-list.outputs.metadata_files }}" + formatted_metadata_file_names=() + + for metadata_file_name in $metadata_file_names + do + if [[ "$metadata_file_name" =~ "../" ]]; then + formatted_metadata_file_names+=(${metadata_file_name#"../"}) + else + formatted_metadata_file_names+=($metadata_file_name) + fi + done + + echo "file_names=${formatted_metadata_file_names[@]}" >> $GITHUB_OUTPUT + + - name: Validate Preview Images + env: + METADATA_FILE_LIST: ${{ steps.reformatted-metadata-file-list.outputs.file_names }} + run: qml_pipeline_utils validate-metadata-preview-images --metadata-files $METADATA_FILE_LIST diff --git a/.gitignore b/.gitignore index 630fb10e61..beb2939413 100644 --- a/.gitignore +++ b/.gitignore @@ -10,3 +10,8 @@ _data/* backreferences .DS_Store demos_community.rst +.venv/ +**/*.egg-info/ + +# Generated after installation +.github/workflows/qml_pipeline_utils/build/ diff --git a/poetry.lock b/poetry.lock index 91d24d395b..adaa1f1493 100644 --- a/poetry.lock +++ b/poetry.lock @@ -1,4 +1,4 @@ -# This file is automatically @generated by Poetry 1.6.1 and should not be changed by hand. +# This file is automatically @generated by Poetry 1.5.1 and should not be changed by hand. [[package]] name = "absl-py" @@ -1986,8 +1986,6 @@ description = "Clang Python Bindings, mirrored from the official LLVM repo: http optional = false python-versions = "*" files = [ - {file = "libclang-16.0.6-1-py2.py3-none-manylinux2014_aarch64.whl", hash = "sha256:88bc7e7b393c32e41e03ba77ef02fdd647da1f764c2cd028e69e0837080b79f6"}, - {file = "libclang-16.0.6-1-py2.py3-none-manylinux2014_armv7l.whl", hash = "sha256:d80ed5827736ed5ec2bcedf536720476fd9d4fa4c79ef0cb24aea4c59332f361"}, {file = "libclang-16.0.6-py2.py3-none-macosx_10_9_x86_64.whl", hash = "sha256:da9e47ebc3f0a6d90fb169ef25f9fbcd29b4a4ef97a8b0e3e3a17800af1423f4"}, {file = "libclang-16.0.6-py2.py3-none-macosx_11_0_arm64.whl", hash = "sha256:e1a5ad1e895e5443e205568c85c04b4608e4e973dae42f4dfd9cb46c81d1486b"}, {file = "libclang-16.0.6-py2.py3-none-manylinux2010_x86_64.whl", hash = "sha256:9dcdc730939788b8b69ffd6d5d75fe5366e3ee007f1e36a99799ec0b0c001492"}, @@ -3028,7 +3026,6 @@ files = [ {file = 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"sha256:81ff539a12457809666fef6624684c008e00ff6bf455b4b89fd00a140eecd640"}, @@ -3038,7 +3035,6 @@ files = [ {file = "Pillow-10.0.0-cp312-cp312-musllinux_1_1_aarch64.whl", hash = "sha256:d50b6aec14bc737742ca96e85d6d0a5f9bfbded018264b3b70ff9d8c33485551"}, {file = "Pillow-10.0.0-cp312-cp312-musllinux_1_1_x86_64.whl", hash = "sha256:00e65f5e822decd501e374b0650146063fbb30a7264b4d2744bdd7b913e0cab5"}, {file = "Pillow-10.0.0-cp312-cp312-win_amd64.whl", hash = "sha256:f31f9fdbfecb042d046f9d91270a0ba28368a723302786c0009ee9b9f1f60199"}, - {file = "Pillow-10.0.0-cp312-cp312-win_arm64.whl", hash = "sha256:1ce91b6ec08d866b14413d3f0bbdea7e24dfdc8e59f562bb77bc3fe60b6144ca"}, {file = "Pillow-10.0.0-cp38-cp38-macosx_10_10_x86_64.whl", hash = "sha256:349930d6e9c685c089284b013478d6f76e3a534e36ddfa912cde493f235372f3"}, {file = "Pillow-10.0.0-cp38-cp38-macosx_11_0_arm64.whl", hash = "sha256:3a684105f7c32488f7153905a4e3015a3b6c7182e106fe3c37fbb5ef3e6994c3"}, {file = "Pillow-10.0.0-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:b4f69b3700201b80bb82c3a97d5e9254084f6dd5fb5b16fc1a7b974260f89f43"}, @@ -4502,7 +4498,7 @@ files = [ ] [package.dependencies] -greenlet = {version = "!=0.4.17", markers = "python_version >= \"3\" and (platform_machine == \"aarch64\" or platform_machine == \"ppc64le\" or platform_machine == \"x86_64\" or platform_machine == \"amd64\" or platform_machine == \"AMD64\" or platform_machine == \"win32\" or platform_machine == \"WIN32\")"} +greenlet = {version = "!=0.4.17", markers = "python_version >= \"3\" and (platform_machine == \"win32\" or platform_machine == \"WIN32\" or platform_machine == \"AMD64\" or platform_machine == \"amd64\" or platform_machine == \"x86_64\" or platform_machine == \"ppc64le\" or platform_machine == \"aarch64\")"} [package.extras] aiomysql = ["aiomysql", "greenlet (!=0.4.17)"] From bb4e726cef16ec66864ac6a215d3aa8f46941942 Mon Sep 17 00:00:00 2001 From: LucasSilbernagel <57023164+LucasSilbernagel@users.noreply.github.com> Date: Wed, 20 Sep 2023 11:22:32 -0400 Subject: [PATCH 23/24] Add hardware section to metadata (#927) ## Related epic https://app.shortcut.com/xanaduai/epic/45372/users-can-see-example-code-that-demonstrates-how-demos-run-on-real-quantum-hardware-from-aws ## Changes - Updated the demo metadata schema to include an optional hardware section (only to include AWS for the time being, but can accommodate more vendors in future) - Added the hardware metadata to 1 demo for testing purposes - Added AWS logo image asset --------- Co-authored-by: Paul Finlay <50180049+doctorperceptron@users.noreply.github.com> Co-authored-by: Rashid N H M <95639609+rashidnhm@users.noreply.github.com> --- _static/hardware_logos/aws.png | Bin 0 -> 28021 bytes .../tutorial_qubit_rotation.metadata.json | 7 +++++ demonstrations_metadata.md | 20 +++++++++++++- .../demo.metadata.schema.0.1.0.json | 7 +++++ .../objects/hardware.schema.0.1.0.json | 25 ++++++++++++++++++ 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zjPHI~XXqp{ranmLHqrSItdq~et$d6qKK!x6PJN1-huRi_+Scpd5ESSYxAqB#vssad zZ~TC$Z7lV@Z~f-u+3z-;;juBY-BWp|$~CoWyXCm9cc_Al9_2b9!zL8uCm|v$Tq&p@ F@P98tNW1_5 literal 0 HcmV?d00001 diff --git a/demonstrations/tutorial_qubit_rotation.metadata.json b/demonstrations/tutorial_qubit_rotation.metadata.json index 83cf4746a3..5ebff321db 100644 --- a/demonstrations/tutorial_qubit_rotation.metadata.json +++ b/demonstrations/tutorial_qubit_rotation.metadata.json @@ -39,5 +39,12 @@ "id": "tutorial_state_preparation", "weight": 1.0 } + ], + "hardware": [ + { + "id": "aws", + "link": "https://pennylane.ai/qml/demos/tutorial_qubit_rotation", + "logo": "/_static/hardware_logos/aws.png" + } ] } diff --git a/demonstrations_metadata.md b/demonstrations_metadata.md index 8cd24ce48c..11865a9f40 100644 --- a/demonstrations_metadata.md +++ b/demonstrations_metadata.md @@ -58,6 +58,13 @@ Below is given an example of a complete metadata file for a demonstration. "id": "tutorial_qubit_rotation", "weight": 1.0 } + ], + "hardware": [ + { + "id": "aws", + "link": "https://pennylane.ai/qml/demos/tutorial_quantum_transfer_learning", + "logo": "/_static/hardware_logos/aws.png" + } ] } ``` @@ -84,6 +91,7 @@ The table below gives details about the fields in the metadata JSON file for ver | `basedOnPapers` | Yes, but can be an empty array | `array` of `string` | An array of the DOIs for the papers this demo is based on. | | `referencedByPapers` | Yes, but can be an empty array | `array` of `string` | An array of the DOIs of any papers that reference this demo. | | `relatedContent` | Yes, but can be an empty array | `array` of `object` | An array of objects describing the content related to this demo. See below for the object structure. | +| `hardware` | No. Can be an empty array. | `array` of `object` | An array of objects representing third-party vendors who can run the demo on their hardware. See below for the object structure. | ### Author Object Properties @@ -125,6 +133,14 @@ The table below gives details about the fields in the metadata JSON file for ver | `id` | Yes | `string` | The id of the content that this relation refers to. For demos, it's the file name of the demo without the extension - i.e., `tutorial_haar_measure`. | | `weight` | Yes | `number` | A number between -1.0 and 1.0 indicating both how closely related these two pieces of content are, and which one it is preferable to encounter first. A value of 1.0 indicates that these two pieces of content are *very* closely related, and this one should be read first, and the linked one second. A value of -1.0 indicates again that these two pieces of content are very closely related, but that the linked one should be read first. A value of 0.0 indicates that these two pieces of content have nothing to do with each other. | +### Hardware Object Properties + +| Name | Is Required | Value Type | Description | +|---|---|---|---| +| `id` | Yes | `enum` | The ID of the hardware vendor | +| `link` | Yes | `string` | Link to run the demo on the vendor's hardware | +| `logo` | Yes | `string` | The URI of the vendor's logo image, whether it be something hosted locally or on another site. | + @@ -202,7 +218,9 @@ The table below gives details about the fields in the metadata JSON file for ver "id": "", "weight": 1.0 } - ] + ], + "relatedContent": [], + "hardware": [] } ``` diff --git a/metadata_schemas/demo.metadata.schema.0.1.0.json b/metadata_schemas/demo.metadata.schema.0.1.0.json index 3a7e28b7fd..7c6c628140 100644 --- a/metadata_schemas/demo.metadata.schema.0.1.0.json +++ b/metadata_schemas/demo.metadata.schema.0.1.0.json @@ -106,6 +106,13 @@ "items": { "$ref": "file:./objects/related.content.schema.0.1.0.json" } + }, + "hardware": { + "description": "An array of objects representing third-party vendors who can run the demo on their hardware. An empty array is allowed.", + "type": "array", + "items": { + "$ref": "file:./objects/hardware.schema.0.1.0.json" + } } }, "required": [ diff --git a/metadata_schemas/objects/hardware.schema.0.1.0.json b/metadata_schemas/objects/hardware.schema.0.1.0.json new file mode 100644 index 0000000000..fefe1e9854 --- /dev/null +++ b/metadata_schemas/objects/hardware.schema.0.1.0.json @@ -0,0 +1,25 @@ +{ + "$schema": "https://json-schema.org/draft/2020-12/schema", + "$id": "hardware.schema.0.1.0.json", + "title": "Hardware", + "description": "Hardware vendors that can run the demo on their hardware", + "type": "object", + "properties": { + "id": { + "description": "The ID of the hardware vendor", + "enum": ["aws"] + }, + "link": { + "description": "Link to run the demo on the vendor's hardware", + "type": "string" + }, + "logo": { + "description": "The URI of the vendor's logo image, whether it be something hosted locally or on another site.", + "type": "string", + "format": "uri-reference" + } + }, + "required": [ + "id", "link", "logo" + ] +} \ No newline at end of file From e150259e47c2349ff32c7a7e3f571093c6c6403e Mon Sep 17 00:00:00 2001 From: soranjh <40344468+soranjh@users.noreply.github.com> Date: Fri, 22 Sep 2023 09:42:00 -0400 Subject: [PATCH 24/24] Update PySCF demo to include initial states (#934) **Title:** **Summary:** Adds a section to the PySCF demo explaining how to import initial states. **Relevant references:** **Possible Drawbacks:** **Related GitHub Issues:** --------- Co-authored-by: Stepan Fomichev --- .../tutorial_qchem_external.metadata.json | 2 +- demonstrations/tutorial_qchem_external.py | 52 +++++++++++++++++-- 2 files changed, 48 insertions(+), 6 deletions(-) diff --git a/demonstrations/tutorial_qchem_external.metadata.json b/demonstrations/tutorial_qchem_external.metadata.json index 12183ba77a..244de7d9bd 100644 --- a/demonstrations/tutorial_qchem_external.metadata.json +++ b/demonstrations/tutorial_qchem_external.metadata.json @@ -6,7 +6,7 @@ } ], "dateOfPublication": "2023-01-03T00:00:00+00:00", - "dateOfLastModification": "2023-01-04T00:00:00+00:00", + "dateOfLastModification": "2023-09-21T00:00:00+00:00", "categories": [ "Quantum Chemistry", "Devices and Performance" ], diff --git a/demonstrations/tutorial_qchem_external.py b/demonstrations/tutorial_qchem_external.py index 7018f30821..fed990b0fb 100644 --- a/demonstrations/tutorial_qchem_external.py +++ b/demonstrations/tutorial_qchem_external.py @@ -19,9 +19,12 @@ The quantum chemistry module in PennyLane, :mod:`qml.qchem `, provides built-in methods to compute molecular integrals, solve Hartree-Fock equations, and construct `fully-differentiable `_ molecular -Hamiltonians. However, there are many other interesting and widely used quantum chemistry libraries out there. Instead of reinventing the wheel, PennyLane lets you to take advantage of various external resources and libraries to build upon existing research. In this demo we will show you how to integrate PennyLane with `PySCF `_ and -`OpenFermion `_ to compute molecular integrals and -construct molecular Hamiltonians. +Hamiltonians. However, there are many other interesting and widely used quantum chemistry libraries +out there. Instead of reinventing the wheel, PennyLane lets you take advantage of various +external resources and libraries to build upon existing research. In this demo we will show you how +to integrate PennyLane with `PySCF `_ and +`OpenFermion `_ to compute molecular integrals, +construct molecular Hamiltonians, and import initial states. Building molecular Hamiltonians ------------------------------- @@ -131,6 +134,43 @@ print(f'Estimated number of non-Clifford gates: {algo.gates:.2e}') print(f'Estimated number of logical qubits: {algo.qubits}') +############################################################################## +# Importing initial states +# ------------------------ +# Simulating molecules with quantum algorithms requires defining an initial state that should have +# non-zero overlap with the molecular ground state. A trivial choice for the initial state is the +# Hartree-Fock state which is obtained by putting the electrons in the lowest-energy molecular +# orbitals. For molecules with a complicated electronic structure, the Hartree-Fock state has +# only a small overlap with the ground state, which makes executing quantum algorithms +# inefficient. +# +# Initial states obtained from affordable post-Hartree-Fock calculations can be used to make the +# quantum workflow more performant. For instance, configuration interaction (CI) and coupled cluster +# (CC) calculations with single and double (SD) excitations can be performed using PySCF and the +# resulting wave function can be used as the initial state in the quantum algorithm. PennyLane +# provides the :func:`~.pennylane.qchem.import_state` function that takes a PySCF solver object, +# extracts the wave function and returns a state vector in the computational basis that can be used +# in a quantum circuit. Let’s look at an example. +# +# First, we run CCSD calculations for the hydrogen molecule to obtain the solver object. + +from pyscf import gto, scf, cc + +mol = gto.M(atom=[['H', (0, 0, 0)], ['H', (0, 0, 0.7)]]) +myhf = scf.RHF(mol).run() +mycc = cc.CCSD(myhf).run() + +############################################################################## +# Then, we use the :func:`~.pennylane.qchem.import_state` function to obtain the +# state vector. + +state = qml.qchem.import_state(mycc) +print(state) + +############################################################################## +# You can verify that this state is a superposition of the Hartree-Fock state and a doubly-excited +# state. + ############################################################################## # Conclusions # ----------- @@ -144,9 +184,11 @@ # the argument ``method=pyscf`` to the :func:`~.pennylane.qchem.molecular_hamiltonian` function. # 2. We can directly use one- and two-electron integrals from PySCF, but we need to convert the # tensor containing the two-electron integrals from chemists' notation to physicists' notation. -# 3. Finally, we can easily convert OpenFermion operators to PennyLane operators using the +# 3. We can easily convert OpenFermion operators to PennyLane operators using the # :func:`~.pennylane.import_operator` function. +# 4. Finally, we can convert PySCF wave functions to PennyLane state vectors using the +# :func:`~.pennylane.qchem.import_state` function. # # About the author # ---------------- -# .. include:: ../_static/authors/soran_jahangiri.txt \ No newline at end of file +# .. include:: ../_static/authors/soran_jahangiri.txt
` @@ -197,6 +203,7 @@ such as benchmarking and characterizing quantum processors. demos/tutorial_qutrits_bernstein_vazirani demos/tutorial_circuit_compilation demos/tutorial_intro_qsvt - demos/tutorial_grovers_algorithm + demos/tutorial_grovers_algorithm + demos/tutorial_apply_qsvt From 2d1cc6cd4b4ad869fbffe2166c0480902a090734 Mon Sep 17 00:00:00 2001 From: Mudit Pandey Date: Tue, 22 Aug 2023 13:16:09 -0400 Subject: [PATCH 03/24] Update diff check workflow to use poetry (#898) * Updated diff check workflow to use poetry * Fix small mistake --- .github/workflows/demo_diff_check.yml | 72 ++++++++++++++++++++++----- 1 file changed, 60 insertions(+), 12 deletions(-) diff --git a/.github/workflows/demo_diff_check.yml b/.github/workflows/demo_diff_check.yml index 53f30c802d..85ceae7fb0 100644 --- a/.github/workflows/demo_diff_check.yml +++ b/.github/workflows/demo_diff_check.yml @@ -25,17 +25,41 @@ jobs: - name: Run Rigetti QVM run: docker run --rm -d -p 5000:5000 rigetti/qvm -S - - name: Set up Python + - name: Set up Python Version uses: actions/setup-python@v2 with: python-version: 3.9 - - name: Install dependencies + - name: Setup Python Environment + id: venv run: | - python -m pip install --upgrade pip wheel - pip install -r requirements.txt - pip install --no-deps -r requirements_no_deps.txt - pip install --index-url https://test.pypi.org/simple/ pennylane-lightning --pre --no-deps --upgrade + if [ ! -d "venv" ]; then python3 -m venv venv; fi + echo "location=${{ github.workspace }}/venv" >> $GITHUB_OUTPUT + echo "${{ github.workspace }}/venv/bin" >> $GITHUB_PATH + + - name: Install Poetry + id: poetry + env: + POETRY_HOME: /tmp/poetry + run: | + curl -sSL https://install.python-poetry.org -o install-poetry.py + python3 install-poetry.py --version 1.5.1 + + $POETRY_HOME/bin/poetry config virtualenvs.create "false" + $POETRY_HOME/bin/poetry config virtualenvs.in-project "true" + + $POETRY_HOME/bin/poetry --version + + echo "bin=${{ env.POETRY_HOME }}/bin/poetry" >> $GITHUB_OUTPUT + + - name: Install Python Dependencies + run: | + source ${{ steps.venv.outputs.location }}/bin/activate + make POETRY_BIN="${{ steps.poetry.outputs.bin }}" UPGRADE_PL=1 environment + + # Adding this install outside of poetry as the dependencies conflict with existing other packages. + # TODO: This needs to be revisited. + ${{ steps.venv.outputs.location }}/bin/python3 -m pip install --no-deps mitiq==0.25.0 - name: Build tutorials run: | @@ -70,17 +94,41 @@ jobs: - name: Run Rigetti QVM run: docker run --rm -d -p 5000:5000 rigetti/qvm -S - - name: Set up Python + - name: Set up Python Version uses: actions/setup-python@v2 with: python-version: 3.9 - - name: Install dependencies + - name: Setup Python Environment + id: venv + run: | + if [ ! -d "venv" ]; then python3 -m venv venv; fi + echo "location=${{ github.workspace }}/venv" >> $GITHUB_OUTPUT + echo "${{ github.workspace }}/venv/bin" >> $GITHUB_PATH + + - name: Install Poetry + id: poetry + env: + POETRY_HOME: /tmp/poetry + run: | + curl -sSL https://install.python-poetry.org -o install-poetry.py + python3 install-poetry.py --version 1.5.1 + + $POETRY_HOME/bin/poetry config virtualenvs.create "false" + $POETRY_HOME/bin/poetry config virtualenvs.in-project "true" + + $POETRY_HOME/bin/poetry --version + + echo "bin=${{ env.POETRY_HOME }}/bin/poetry" >> $GITHUB_OUTPUT + + - name: Install Python Dependencies run: | - python -m pip install --upgrade pip wheel - pip install -r requirements.txt - pip install --no-deps -r requirements_no_deps.txt - pip install pennylane-lightning + source ${{ steps.venv.outputs.location }}/bin/activate + make POETRY_BIN="${{ steps.poetry.outputs.bin }}" UPGRADE_PL=0 environment + + # Adding this install outside of poetry as the dependencies conflict with existing other packages. + # TODO: This needs to be revisited. + ${{ steps.venv.outputs.location }}/bin/python3 -m pip install --no-deps mitiq==0.25.0 - name: Build tutorials run: | From cc22fd18ff5ae72cd28c82525feefd95b2e62ce1 Mon Sep 17 00:00:00 2001 From: Guillermo Alonso-Linaje <65235481+KetpuntoG@users.noreply.github.com> Date: Tue, 22 Aug 2023 17:58:50 -0400 Subject: [PATCH 04/24] Update tutorial_mitigation_advantage.py (#899) --- demonstrations/tutorial_mitigation_advantage.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/demonstrations/tutorial_mitigation_advantage.py b/demonstrations/tutorial_mitigation_advantage.py index b8a4d13e44..9d4338375d 100644 --- a/demonstrations/tutorial_mitigation_advantage.py +++ b/demonstrations/tutorial_mitigation_advantage.py @@ -57,7 +57,7 @@ The system is described by the positive coupling strength :math:`J` and transverse field `h`. The time evolution is approximated by trotterization of the time evolution operator -.. math:: U(T) \approx \left(\prod_{\langle qp \rangle} e^{i \delta t J Z_q Z_p} \prod_{q} e^{-i \delta t J X_q} \right)^{\frac{T}{\delta t}} +.. math:: U(T) \approx \left(\prod_{\langle qp \rangle} e^{i \delta t J Z_q Z_p} \prod_{q} e^{-i \delta t h X_q} \right)^{\frac{T}{\delta t}} for an evolution time :math:`T` and a Trotter step size :math:`\delta t`. That means the circuit of concern here is a series of consecutive :math:`\text{RZZ}(\theta_J)` and :math:`\text{RX}(\theta_h)` rotations. The corresponding From a76dfc2cb25d8a68946660a9389b893c83305dde Mon Sep 17 00:00:00 2001 From: Ivana Kurecic Date: Wed, 23 Aug 2023 01:51:42 +0200 Subject: [PATCH 05/24] removing issue (#881) Co-authored-by: Guillermo Alonso-Linaje <65235481+KetpuntoG@users.noreply.github.com> --- demonstrations/tutorial_givens_rotations.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/demonstrations/tutorial_givens_rotations.py b/demonstrations/tutorial_givens_rotations.py index 08aa5abeb6..e788c4f18c 100644 --- a/demonstrations/tutorial_givens_rotations.py +++ b/demonstrations/tutorial_givens_rotations.py @@ -308,7 +308,7 @@ def circuit2(x, y): # 0 & \cos (\theta) & e^{i\phi}\sin (\theta) & 0\\ # 0 & e^{-i\phi}\sin(\theta) & -\cos(\theta) & 0\\ # 0 & 0 & 0 & 1 -# \end{pmatrix},\\[12pt] +# \end{pmatrix},\\ # # U_2(\theta) &= \begin{pmatrix} # 1 & 0 & 0 & 0\\ From a5097811cf75a3e8094f69a11dfa371a17edb874 Mon Sep 17 00:00:00 2001 From: David Wierichs Date: Thu, 24 Aug 2023 17:31:35 +0200 Subject: [PATCH 06/24] fix arXiv link (#902) --- demonstrations/tutorial_optimal_control.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/demonstrations/tutorial_optimal_control.py b/demonstrations/tutorial_optimal_control.py index 7dc212452e..2b30c9d808 100644 --- a/demonstrations/tutorial_optimal_control.py +++ b/demonstrations/tutorial_optimal_control.py @@ -824,7 +824,7 @@ def node(params): # T. Caneva, M. Murphy, T. Calarco, R. Fazio, S. Montangero, V. Giovannetti and G. Santoro # "Optimal Control at the Quantum Speed Limit" # `Phys. Rev. Lett. 103, 240501 `__, -# `arxiv:0902.4193 __, 2009 +# `arxiv:0902.4193 `__, 2009 # # .. [#KingmaBa14] # From 69af812246cceaba9dfe5eb814229b4a0d0d7f80 Mon Sep 17 00:00:00 2001 From: Guillermo Alonso-Linaje <65235481+KetpuntoG@users.noreply.github.com> Date: Mon, 28 Aug 2023 15:18:33 -0400 Subject: [PATCH 07/24] learning2learn executable (#901) * removing code * Update tutorial_learning2learn.py * Update demos_qml.rst * Update tutorial_learning2learn.metadata.json --- ... => tutorial_learning2learn.metadata.json} | 4 +- ...ng2learn.py => tutorial_learning2learn.py} | 217 ------------------ demos_qml.rst | 4 +- 3 files changed, 4 insertions(+), 221 deletions(-) rename demonstrations/{learning2learn.metadata.json => tutorial_learning2learn.metadata.json} (88%) rename demonstrations/{learning2learn.py => tutorial_learning2learn.py} (79%) diff --git a/demonstrations/learning2learn.metadata.json b/demonstrations/tutorial_learning2learn.metadata.json similarity index 88% rename from demonstrations/learning2learn.metadata.json rename to demonstrations/tutorial_learning2learn.metadata.json index bfcf498d97..dd72b26938 100644 --- a/demonstrations/learning2learn.metadata.json +++ b/demonstrations/tutorial_learning2learn.metadata.json @@ -6,7 +6,7 @@ } ], "dateOfPublication": "2021-03-02T00:00:00+00:00", - "dateOfLastModification": "2021-09-15T00:00:00+00:00", + "dateOfLastModification": "2023-08-28T00:00:00+00:00", "categories": [ "Quantum Machine Learning" ], @@ -19,7 +19,7 @@ ], "seoDescription": "Use a classical recurrent neural network to initilize the parameters of a variational quatum algorithm.", "doi": "", - "canonicalURL": "/qml/demos/learning2learn", + "canonicalURL": "/qml/demos/tutorial_learning2learn", "references": [], "basedOnPapers": [], "referencedByPapers": [], diff --git a/demonstrations/learning2learn.py b/demonstrations/tutorial_learning2learn.py similarity index 79% rename from demonstrations/learning2learn.py rename to demonstrations/tutorial_learning2learn.py index c591befb6b..5bb97a1e61 100644 --- a/demonstrations/learning2learn.py +++ b/demonstrations/tutorial_learning2learn.py @@ -119,9 +119,6 @@ Check out this great tutorial on how to use QAOA for solving graph problems: https://pennylane.ai/qml/demos/tutorial_qaoa_intro.html -.. note:: - Running the tutorial (excluding the Appendix) requires approx. ~13m. - """ ###################################################################### @@ -194,12 +191,6 @@ def generate_graphs(n_graphs, n_nodes, p_edge): nx.draw(graphs[0]) -###################################################################### -# .. figure:: ../demonstrations/learning2learn/rendered_Graph0.png -# :align: center -# :width: 70% -# :target: javascript:void(0); -# ###################################################################### # Variational Quantum Circuit: QAOA @@ -262,15 +253,6 @@ def hamiltonian(params, **kwargs): # Evaluate th QAOA instance just created with some angles. print(cost(x)) -############################################################################## -# .. rst-class:: sphx-glr-script-out -# -# Out: -# -# .. code-block:: none -# -# tf.Tensor(-3.193267957255582, shape=(), dtype=float64) -# ###################################################################### @@ -476,46 +458,6 @@ def train_step(graph_cost): print(f" > Graph {i+1}/{len(graph_cost_list)} - Loss: {loss[0][0]}") print(f" >> Mean Loss during epoch: {np.mean(total_loss)}") -############################################################################## -# .. rst-class:: sphx-glr-script-out -# -# Out: -# -# .. code-block:: none -# -# Epoch 1 -# > Graph 1/20 - Loss: -1.6641689538955688 -# > Graph 6/20 - Loss: -1.4186843633651733 -# > Graph 11/20 - Loss: -1.3757232427597046 -# > Graph 16/20 - Loss: -1.294339656829834 -# >> Mean Loss during epoch: -1.7352586269378663 -# Epoch 2 -# > Graph 1/20 - Loss: -2.119091749191284 -# > Graph 6/20 - Loss: -1.4789190292358398 -# > Graph 11/20 - Loss: -1.3779840469360352 -# > Graph 16/20 - Loss: -1.2963457107543945 -# >> Mean Loss during epoch: -1.8252217948436738 -# Epoch 3 -# > Graph 1/20 - Loss: -2.1322619915008545 -# > Graph 6/20 - Loss: -1.459418535232544 -# > Graph 11/20 - Loss: -1.390620470046997 -# > Graph 16/20 - Loss: -1.3165746927261353 -# >> Mean Loss during epoch: -1.8328069806098939 -# Epoch 4 -# > Graph 1/20 - Loss: -2.1432175636291504 -# > Graph 6/20 - Loss: -1.476362943649292 -# > Graph 11/20 - Loss: -1.3938289880752563 -# > Graph 16/20 - Loss: -1.3140206336975098 -# >> Mean Loss during epoch: -1.8369774043560028 -# Epoch 5 -# > Graph 1/20 - Loss: -2.1429405212402344 -# > Graph 6/20 - Loss: -1.477513074874878 -# > Graph 11/20 - Loss: -1.3909202814102173 -# > Graph 16/20 - Loss: -1.315887689590454 -# >> Mean Loss during epoch: -1.8371947884559632 -# - - ###################################################################### # As you can see, the Loss for each graph keeps decreasing across epochs, # indicating that the training routine is working correctly. @@ -537,14 +479,6 @@ def train_step(graph_cost): nx.draw(new_graph) -###################################################################### -# .. figure:: ../demonstrations/learning2learn/rendered_Graph1.png -# :align: center -# :width: 70% -# :target: javascript:void(0); -# - - ###################################################################### # Then we apply the trained RNN to this new graph, saving intermediate # results coming from all the recurrent iterations in the network. @@ -589,11 +523,6 @@ def train_step(graph_cost): plt.show() ###################################################################### -# .. figure:: ../demonstrations/learning2learn/rendered_LossLSTM.png -# :align: center -# :width: 70% -# :target: javascript:void(0); -# # That’s remarkable! The RNN learned to propose new parameters such that # the MaxCut cost is minimized very rapidly: in just a few iterations the # loss reaches a minimum. Actually, it takes just a single step for the LSTM @@ -640,30 +569,6 @@ def train_step(graph_cost): print(f"Final cost function: {new_cost(x).numpy()}\nOptimized angles: {x.numpy()}") ############################################################################## -# .. rst-class:: sphx-glr-script-out -# -# Out: -# -# .. code-block:: none -# -# Step 1 - Loss = -4.1700805 -# Step 2 - Loss = -4.67503588 -# Step 3 - Loss = -5.09949909 -# Step 4 - Loss = -5.40388533 -# Step 5 - Loss = -5.59529203 -# Step 6 - Loss = -5.70495197 -# Step 7 - Loss = -5.7642561 -# Step 8 - Loss = -5.79533198 -# Step 9 - Loss = -5.81138752 -# Step 10 - Loss = -5.81966529 -# Step 11 - Loss = -5.82396722 -# Step 12 - Loss = -5.82624537 -# Step 13 - Loss = -5.82749126 -# Step 14 - Loss = -5.82820626 -# Step 15 - Loss = -5.82864379 -# Final cost function: -5.828932361904984 -# Optimized angles: [[ 0.5865477 ] -# [-0.3228858]] # fig, ax = plt.subplots() @@ -679,13 +584,6 @@ def train_step(graph_cost): ax.set_xticks([0, 5, 10, 15, 20]); plt.show() -###################################################################### -# .. figure:: ../demonstrations/learning2learn/rendered_LossConfrontation.png -# :align: center -# :width: 70% -# :target: javascript:void(0); -# - ###################################################################### # *Hurray!* 🎉🎉 @@ -852,30 +750,6 @@ def train_step(graph_cost): print(f" > Graph {i+1}/{len(gs_cost_list)} - Loss: {loss}") print(f" >> Mean Loss during epoch: {np.mean(total_loss)}") -############################################################################## -# .. rst-class:: sphx-glr-script-out -# -# Out: -# -# .. code-block:: none -# -# Epoch 1 -# > Graph 1/15 - Loss: [[-1.4876363]] -# > Graph 6/15 - Loss: [[-1.8590403]] -# > Graph 11/15 - Loss: [[-1.7644017]] -# >> Mean Loss during epoch: -1.9704322338104248 -# Epoch 2 -# > Graph 1/15 - Loss: [[-1.8650053]] -# > Graph 6/15 - Loss: [[-1.9578737]] -# > Graph 11/15 - Loss: [[-1.8377447]] -# >> Mean Loss during epoch: -2.092947308222453 -# Epoch 3 -# > Graph 1/15 - Loss: [[-1.9009062]] -# > Graph 6/15 - Loss: [[-1.9726204]] -# > Graph 11/15 - Loss: [[-1.8668792]] -# >> Mean Loss during epoch: -2.1162660201390584 -# - ###################################################################### # Let’s check if this hybrid model eventually learned a good heuristic to @@ -892,14 +766,6 @@ def train_step(graph_cost): nx.draw(new_graph) -###################################################################### -# .. figure:: ../demonstrations/learning2learn/rendered_Graph10.png -# :align: center -# :width: 70% -# :target: javascript:void(0); -# - - ###################################################################### # We call the trained recurrent LSTM on this graph, saving not only the # last, but all intermediate guesses for the parameters. @@ -933,14 +799,6 @@ def train_step(graph_cost): ax.set_xticks([0, 5, 10, 15, 20]); plt.show() -###################################################################### -# .. figure:: ../demonstrations/learning2learn/rendered_LossGeneralization.png -# :align: center -# :width: 70% -# :target: javascript:void(0); -# - - ###################################################################### # Again, we can confirm that the custom optimizer based on the LSTM quickly reaches a good # value of the loss function, and also achieve good generalization performances, since @@ -988,13 +846,6 @@ def train_step(graph_cost): plt.show() ###################################################################### -# .. figure:: ../demonstrations/learning2learn/rendered_LossLandscape.png -# :align: center -# :width: 70% -# :target: javascript:void(0); -# -# -# # # Ideas for creating a Keras Layer and Keras Model # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ @@ -1065,54 +916,6 @@ def call(self, inputs): model.summary() - -############################################################################## -# .. rst-class:: sphx-glr-script-out -# -# Out: -# -# .. code-block:: none -# -# Model: "functional_1" -# __________________________________________________________________________________________________ -# Layer (type) Output Shape Param # Connected to -# ================================================================================================== -# input_1 (InputLayer) [(None, 1)] 0 -# __________________________________________________________________________________________________ -# input_2 (InputLayer) [(None, 2)] 0 -# __________________________________________________________________________________________________ -# input_3 (InputLayer) [(None, 2)] 0 -# __________________________________________________________________________________________________ -# input_4 (InputLayer) [(None, 2)] 0 -# __________________________________________________________________________________________________ -# qrnn (QRNN) [(1, 1), 48 input_1[0][0] -# (None, 2), input_2[0][0] -# (None, 2), input_3[0][0] -# (None, 2)] input_4[0][0] -# qrnn[0][0] -# qrnn[0][1] -# qrnn[0][2] -# qrnn[0][3] -# qrnn[1][0] -# qrnn[1][1] -# qrnn[1][2] -# qrnn[1][3] -# __________________________________________________________________________________________________ -# tf.math.multiply (TFOpLambda) (1, 1) 0 qrnn[0][0] -# __________________________________________________________________________________________________ -# tf.math.multiply_1 (TFOpLambda) (1, 1) 0 qrnn[1][0] -# __________________________________________________________________________________________________ -# tf.math.multiply_2 (TFOpLambda) (1, 1) 0 qrnn[2][0] -# __________________________________________________________________________________________________ -# average_147 (Average) (1, 1) 0 tf.math.multiply[0][0] -# tf.math.multiply_1[0][0] -# tf.math.multiply_2[0][0] -# ================================================================================================== -# Total params: 48 -# Trainable params: 48 -# Non-trainable params: 0 -# - ###################################################################### # A basic training routine for the ``Keras Model`` just created: # @@ -1145,26 +948,6 @@ def call(self, inputs): for t, s in zip(pred, ["out0", "out1", "out2", "Loss"]): print(f" >{s}: {t.numpy()}") -############################################################################## -# .. rst-class:: sphx-glr-script-out -# -# Out: -# -# .. code-block:: none -# -# Step 1 - Loss = [[-1.5563084]] - Cost = -4.762684301954701 -# Step 2 - Loss = [[-1.5649065]] - Cost = -4.799981173473755 -# Step 3 - Loss = [[-1.5741502]] - Cost = -4.840036354736862 -# Step 4 - Loss = [[-1.5841404]] - Cost = -4.883246647056216 -# Step 5 - Loss = [[-1.5948243]] - Cost = -4.929228976649736 -# Final Loss: [[-1.5948243]] -# Final Outs: -# >out0: [[-0.01041588 0.01016874]] -# >out1: [[-0.04530389 0.38148248]] -# >out2: [[-0.10258182 0.4134117 ]] -# >Loss: [[-1.5948243]] -# - ###################################################################### # .. note:: # This code works only for a single graph at a time, since a graph was diff --git a/demos_qml.rst b/demos_qml.rst index 154692df4d..e1c394711f 100644 --- a/demos_qml.rst +++ b/demos_qml.rst @@ -77,7 +77,7 @@ IBM, Google, Rigetti, and many more—or on real hardware devices. .. gallery-item:: :tooltip: Meta-learning technique for variational quantum algorithms. :figure: demonstrations/learning2learn/l2l_thumbnail.png - :description: :doc:`demos/learning2learn` + :description: :doc:`demos/tutorial_learning2learn` :tags: tensorflow .. gallery-item:: @@ -167,7 +167,7 @@ IBM, Google, Rigetti, and many more—or on real hardware devices. demos/tutorial_QGAN demos/quantum_neural_net demos/tutorial_qgrnn - demos/learning2learn + demos/tutorial_learning2learn demos/tutorial_quanvolution demos/ensemble_multi_qpu demos/tutorial_quantum_gans From 9bc33318a804ccb905046889d91c5d462dfa6d41 Mon Sep 17 00:00:00 2001 From: Mudit Pandey Date: Thu, 31 Aug 2023 17:26:27 -0400 Subject: [PATCH 08/24] Merge dev with master for v0.32 (#913) As title says --------- Co-authored-by: Josh Izaac Co-authored-by: GitHub Nightly Merge Action Co-authored-by: Rashid N H M <95639609+rashidnhm@users.noreply.github.com> Co-authored-by: Jay Soni Co-authored-by: soranjh <40344468+soranjh@users.noreply.github.com> Co-authored-by: Romain Moyard Co-authored-by: David Wierichs Co-authored-by: trbromley Co-authored-by: Christina Lee Co-authored-by: Matthew Silverman Co-authored-by: lillian542 <38584660+lillian542@users.noreply.github.com> Co-authored-by: Tom Bromley <49409390+trbromley@users.noreply.github.com> Co-authored-by: Guillermo Alonso-Linaje <65235481+KetpuntoG@users.noreply.github.com> Co-authored-by: CatalinaAlbornoz Co-authored-by: soranjh Co-authored-by: ixfoduap <40441298+ixfoduap@users.noreply.github.com> Co-authored-by: Diego <67476785+DSGuala@users.noreply.github.com> Co-authored-by: Utkarsh --- .github/workflows/build-branch.yml | 14 +- README.md | 4 +- conf.py | 3 +- demonstrations/ahs_aquila.py | 2 +- demonstrations/quantum_neural_net.py | 2 +- demonstrations/tutorial_QGAN.py | 6 +- demonstrations/tutorial_apply_qsvt.py | 2 +- .../tutorial_expressivity_fourier_series.py | 2 +- demonstrations/tutorial_general_parshift.py | 2 +- demonstrations/tutorial_mbqc.py | 14 +- .../tutorial_measurement_optimize.py | 179 +- .../tutorial_ml_classical_shadows.py | 2 +- demonstrations/tutorial_pasqal.py | 2 +- demonstrations/tutorial_photonics.py | 30 +- demonstrations/tutorial_qgrnn.py | 2 +- demonstrations/tutorial_quantum_chemistry.py | 7 +- demonstrations/tutorial_sc_qubits.py | 4 +- .../tutorial_variational_classifier.py | 2 +- demonstrations/tutorial_vqe.py | 17 +- demonstrations/tutorial_vqe_qng.py | 18 +- demonstrations/vqe_parallel.py | 48 +- poetry.lock | 1959 ++++++++--------- pyproject.toml | 20 +- 23 files changed, 1135 insertions(+), 1206 deletions(-) diff --git a/.github/workflows/build-branch.yml b/.github/workflows/build-branch.yml index 6d2929a224..c71df7c2cf 100644 --- a/.github/workflows/build-branch.yml +++ b/.github/workflows/build-branch.yml @@ -305,6 +305,9 @@ jobs: # Adding this install outside of poetry as the dependencies conflict with existing other packages. # TODO: This needs to be revisited. + # The following needs to be done in order for this dependency to be moved into pyproject.toml: + # - PennyLane-Cirq needs to support cirq >= 1.1.0 + # - PennyLane needs to support numpy ~> 1.24 ${{ steps.venv.outputs.location }}/bin/python3 -m pip install --no-deps mitiq==0.25.0 - name: Download Worker Load Data Artifact @@ -432,17 +435,6 @@ jobs: retention-days: 1 path: /tmp/execution_times - # These are files that are generated as part of sphinx-build but are not needed and supported on the live website - # There does not seem to be an option to "not" generate them, therefore this step deletes these files before they - # are published to the live website. - - name: Update sitemap.xml - run: | - ${{ steps.venv.outputs.location }}/bin/qml_pipeline_utils \ - clean-sitemap \ - --build-dir="${{ github.workspace }}/_build/html" \ - --html-files="demos/sg_execution_times.html" \ - --verbose - # Removes built html files that are not relevant to current node # See documentation in github_job_scheduler.py for more details. - name: Clean HTML Files diff --git a/README.md b/README.md index 687759666a..ffd7b2a7fc 100644 --- a/README.md +++ b/README.md @@ -242,10 +242,12 @@ Add the new dependency in the `[tool.poetry.group.executable-dependencies.depend Once pyproject.toml files have been updated, the poetry.lock file needs to be refreshed: ```bash -poetry lock +poetry lock --no-update ``` This command will ensure that there are no dependency conflicts with any other package, and everything works. +The `--no-update` ensures existing package versions are not bumped as part of the locking process. + If the dependency change is required in prod, open the PR against `master`, or if it's only required in dev, then open the PR against the `dev` branch, which will be synced to master on the next release of PennyLane. diff --git a/conf.py b/conf.py index 356da42629..62506c7143 100644 --- a/conf.py +++ b/conf.py @@ -50,8 +50,7 @@ "sphinx.ext.intersphinx", "sphinx.ext.mathjax", "sphinx.ext.ifconfig", - "sphinx_gallery.gen_gallery", - "sphinx_sitemap", + "sphinx_gallery.gen_gallery" ] diff --git a/demonstrations/ahs_aquila.py b/demonstrations/ahs_aquila.py index 22f1bde682..1437833138 100644 --- a/demonstrations/ahs_aquila.py +++ b/demonstrations/ahs_aquila.py @@ -613,7 +613,7 @@ def circuit(params): start_val = amplitude[0] stop_val = amplitude[-1] max_val = np.max(amplitude) -max_rate = np.max([(amplitude[i + 1] - amplitude[i]) / timestep for i in range(len(times))]) +max_rate = np.max([(amplitude[i + 1] - amplitude[i]) / timestep for i in range(len(times)-1)]) print(f"start value: {start_val:.3} MHz") print(f"stop value: {stop_val:.3} MHz") diff --git a/demonstrations/quantum_neural_net.py b/demonstrations/quantum_neural_net.py index bc88c3f26d..8d6a48aa62 100644 --- a/demonstrations/quantum_neural_net.py +++ b/demonstrations/quantum_neural_net.py @@ -84,7 +84,7 @@ def quantum_neural_net(var, x): for v in var: layer(v) - return qml.expval(qml.X(0)) + return qml.expval(qml.QuadX(0)) ############################################################################## diff --git a/demonstrations/tutorial_QGAN.py b/demonstrations/tutorial_QGAN.py index 9700c95640..a12491a328 100644 --- a/demonstrations/tutorial_QGAN.py +++ b/demonstrations/tutorial_QGAN.py @@ -204,7 +204,7 @@ def gen_cost(gen_weights): # We begin by creating the optimizer: opt = tf.keras.optimizers.SGD(0.4) - +opt.build([disc_weights, gen_weights]) ############################################################################## # In the first stage of training, we optimize the discriminator while @@ -213,7 +213,7 @@ def gen_cost(gen_weights): cost = lambda: disc_cost(disc_weights) for step in range(50): - opt.minimize(cost, disc_weights) + opt.minimize(cost, [disc_weights]) if step % 5 == 0: cost_val = cost().numpy() print("Step {}: cost = {}".format(step, cost_val)) @@ -243,7 +243,7 @@ def gen_cost(gen_weights): cost = lambda: gen_cost(gen_weights) for step in range(50): - opt.minimize(cost, gen_weights) + opt.minimize(cost, [gen_weights]) if step % 5 == 0: cost_val = cost().numpy() print("Step {}: cost = {}".format(step, cost_val)) diff --git a/demonstrations/tutorial_apply_qsvt.py b/demonstrations/tutorial_apply_qsvt.py index b7c9dde258..bcba41884f 100644 --- a/demonstrations/tutorial_apply_qsvt.py +++ b/demonstrations/tutorial_apply_qsvt.py @@ -341,7 +341,7 @@ def real_u(A, phi): @qml.qnode(qml.device("default.qubit", wires=["ancilla1", "ancilla2", 0, 1, 2])) def linear_system_solver_circuit(phi): - qml.QubitStateVector(normalized_b, wires=[1, 2]) + qml.StatePrep(normalized_b, wires=[1, 2]) real_u(A.T, phi) # invert the singular values of A transpose to get A^-1 return qml.state() diff --git a/demonstrations/tutorial_expressivity_fourier_series.py b/demonstrations/tutorial_expressivity_fourier_series.py index be09145d18..c07db29a6c 100644 --- a/demonstrations/tutorial_expressivity_fourier_series.py +++ b/demonstrations/tutorial_expressivity_fourier_series.py @@ -800,7 +800,7 @@ def quantum_model(weights, x): W(weights[0]) S(x) W(weights[1]) - return qml.expval(qml.X(wires=0)) + return qml.expval(qml.QuadX(wires=0)) def random_weights(): return np.random.normal(size=(2, 5 * n_ansatz_layers), loc=0, scale=var) diff --git a/demonstrations/tutorial_general_parshift.py b/demonstrations/tutorial_general_parshift.py index 0f72664802..edfd0baa81 100644 --- a/demonstrations/tutorial_general_parshift.py +++ b/demonstrations/tutorial_general_parshift.py @@ -143,7 +143,7 @@ def make_cost(N, seed): @qml.qnode(dev, interface="jax") def cost(x): """Cost function on N qubits with N frequencies.""" - qml.QubitStateVector(random_state(N, seed), wires=dev.wires) + qml.StatePrep(random_state(N, seed), wires=dev.wires) for w in dev.wires: qml.RZ(x, wires=w, id="x") return qml.expval(qml.Hermitian(random_observable(N, seed), wires=dev.wires)) diff --git a/demonstrations/tutorial_mbqc.py b/demonstrations/tutorial_mbqc.py index f094c1863b..754bec1b6a 100644 --- a/demonstrations/tutorial_mbqc.py +++ b/demonstrations/tutorial_mbqc.py @@ -162,7 +162,7 @@ def cluster_state(): @qml.qnode(dev, interface="autograd") def one_bit_teleportation(input_state): # Prepare the input state - qml.QubitStateVector(input_state, wires=0) + qml.StatePrep(input_state, wires=0) # Prepare the cluster state qml.Hadamard(wires=1) @@ -305,7 +305,7 @@ def generate_random_state(n=1): @qml.qnode(dev, interface="autograd") def RZ(theta, input_state): # Prepare the input state - qml.QubitStateVector(input_state, wires=0) + qml.StatePrep(input_state, wires=0) # Perform the Rz rotation qml.RZ(theta, wires=0) @@ -326,7 +326,7 @@ def RZ(theta, input_state): @qml.qnode(mbqc_dev, interface="autograd") def RZ_MBQC(theta, input_state): # Prepare the input state - qml.QubitStateVector(input_state, wires=0) + qml.StatePrep(input_state, wires=0) # Prepare the cluster state qml.Hadamard(wires=1) @@ -367,7 +367,7 @@ def RZ_MBQC(theta, input_state): @qml.qnode(dev, interface="autograd") def RX(theta, input_state): # Prepare the input state - qml.QubitStateVector(input_state, wires=0) + qml.StatePrep(input_state, wires=0) # Perform the Rz rotation qml.RX(theta, wires=0) @@ -382,7 +382,7 @@ def RX(theta, input_state): @qml.qnode(mbqc_dev, interface="autograd") def RX_MBQC(theta, input_state): # Prepare the input state - qml.QubitStateVector(input_state, wires=0) + qml.StatePrep(input_state, wires=0) # Prepare the cluster state qml.Hadamard(wires=1) @@ -447,7 +447,7 @@ def RX_MBQC(theta, input_state): @qml.qnode(dev, interface="autograd") def CNOT(input_state): # Prepare the input state - qml.QubitStateVector(input_state, wires=[0, 1]) + qml.StatePrep(input_state, wires=[0, 1]) qml.CNOT(wires=[0, 1]) return qml.density_matrix(wires=[0, 1]) @@ -459,7 +459,7 @@ def CNOT(input_state): @qml.qnode(mbqc_dev, interface="autograd") def CNOT_MBQC(input_state): # Prepare the input state - qml.QubitStateVector(input_state, wires=[0, 1]) + qml.StatePrep(input_state, wires=[0, 1]) # Prepare the cluster state qml.Hadamard(wires=2) diff --git a/demonstrations/tutorial_measurement_optimize.py b/demonstrations/tutorial_measurement_optimize.py index 9987a644ae..ad9b10cec8 100644 --- a/demonstrations/tutorial_measurement_optimize.py +++ b/demonstrations/tutorial_measurement_optimize.py @@ -12,7 +12,7 @@ tutorial_quantum_chemistry Building molecular Hamiltonians tutorial_qaoa_intro Intro to QAOA -*Author: Josh Izaac — Posted: 18 January 2021. Last updated: 8 April 2021.* +*Author: Josh Izaac — Posted: 18 January 2021. Last updated: 29 August 2023.* The variational quantum eigensolver (VQE) is the OG variational quantum algorithm. Harnessing near-term quantum hardware to solve for the electronic structure of molecules, VQE is *the* @@ -101,21 +101,22 @@ For small molecules, the VQE algorithm scales and performs exceedingly well. For example, for the Hydrogen molecule :math:`\text{H}_2`, the final Hamiltonian in its qubit representation -has 15 terms that need to be measured. Let's generate this Hamiltonian from the electronic -structure file :download:`h2.xyz `, -to verify the number of terms. In this tutorial, we use the :func:`~.pennylane.qchem.read_structure` -function to read the geometry of the molecule from an external file. +has 15 terms that need to be measured. Let's obtain the Hamiltonian from +`PennyLane's dataset library `__ +to verify the number of terms. In this tutorial, we use the :func:`~.pennylane.data.load` +function to download the dataset of the molecule. """ import functools +import warnings from pennylane import numpy as np import pennylane as qml np.random.seed(42) -symbols, coordinates = qml.qchem.read_structure("h2.xyz") -H, num_qubits = qml.qchem.molecular_hamiltonian(symbols, coordinates) +dataset = qml.data.load('qchem', molname="H2", bondlength=0.7)[0] +H, num_qubits = dataset.hamiltonian, len(dataset.hamiltonian.wires) print("Required number of qubits:", num_qubits) print(H) @@ -157,10 +158,11 @@ def cost_circuit(params): print("Number of quantum evaluations:", tracker.totals['executions']) ############################################################################## -# How about a larger molecule? Let's try the water molecule :download:`h2o.xyz `: +# How about a larger molecule? Let's try the +# `water molecule `__: -symbols, coordinates = qml.qchem.read_structure("h2o.xyz") -H, num_qubits = qml.qchem.molecular_hamiltonian(symbols, coordinates) +dataset = qml.data.load('qchem', molname="H2O")[0] +H, num_qubits = dataset.hamiltonian, len(dataset.hamiltonian.wires) print("Required number of qubits:", num_qubits) print("Number of Hamiltonian terms/required measurements:", len(H.ops)) @@ -170,7 +172,7 @@ def cost_circuit(params): ############################################################################## # Simply going from two atoms in :math:`\text{H}_2` to three in :math:`\text{H}_2 \text{O}` -# resulted in over triple the number of qubits required and 2110 measurements that must be made! +# resulted in over triple the number of qubits required and 1086 measurements that must be made! # # We can see that as the size of our molecule increases, we run into a problem: larger molecules # result in Hamiltonians that not only require a larger number of qubits :math:`N` in their @@ -567,27 +569,6 @@ def circuit(weights): qml.PauliY(0) @ qml.PauliY(1) @ qml.PauliX(2) @ qml.PauliX(3) ] -G = nx.Graph() - -# add the terms to the graph -G.add_nodes_from(terms) - -# add QWC edges -G.add_edges_from([ - [terms[0], terms[1]], # Z0 <--> Z0 Z1 - [terms[0], terms[2]], # Z0 <--> Z0 Z1 Z2 - [terms[0], terms[3]], # Z0 <--> Z0 Z1 Z2 Z3 - [terms[1], terms[2]], # Z0 Z1 <--> Z0 Z1 Z2 - [terms[2], terms[3]], # Z0 Z1 Z2 <--> Z0 Z1 Z2 Z3 - [terms[1], terms[3]], # Z0 Z1 <--> Z0 Z1 Z2 Z3 - [terms[0], terms[4]], # Z0 <--> X2 X3 - [terms[1], terms[4]], # Z0 Z1 <--> X2 X3 - [terms[4], terms[5]], # X2 X3 <--> Y0 X2 X3 - [terms[4], terms[6]], # X2 X3 <--> Y0 Y1 X2 X3 - [terms[5], terms[6]], # Y0 X2 X3 <--> Y0 Y1 X2 X3 -]) - - def format_pauli_word(term): """Convenience function that nicely formats a PennyLane tensor observable as a Pauli word""" @@ -596,53 +577,81 @@ def format_pauli_word(term): return f"{term.name[-1]}{term.wires.tolist()[0]}" -plt.margins(x=0.1) -nx.draw( - G, - labels={node: format_pauli_word(node) for node in terms}, - with_labels=True, - node_size=500, - font_size=8, - node_color="#9eded1", - edge_color="#c1c1c1" -) - -############################################################################## -# We can now generate the complement graph (compare this to our handdrawn -# version above!): - -C = nx.complement(G) -coords = nx.spring_layout(C) - -nx.draw( - C, - coords, - labels={node: format_pauli_word(node) for node in terms}, - with_labels=True, - node_size=500, - font_size=8, - node_color="#9eded1", - edge_color="#c1c1c1" -) - +G = nx.Graph() -############################################################################## -# Now that we have the complement graph, we can perform a greedy coloring to -# determine the minimum number of QWC groups: - -groups = nx.coloring.greedy_color(C, strategy="largest_first") - -# plot the complement graph with the greedy colouring -nx.draw( - C, - coords, - labels={node: format_pauli_word(node) for node in terms}, - with_labels=True, - node_size=500, - font_size=8, - node_color=[("#9eded1", "#aad4f0")[groups[node]] for node in C], - edge_color="#c1c1c1" -) +with warnings.catch_warnings(): + # Muting irrelevant warnings + warnings.filterwarnings( + "ignore", + message="The behaviour of operator ", + category=UserWarning, + ) + + # add the terms to the graph + G.add_nodes_from(terms) + + # add QWC edges + G.add_edges_from([ + [terms[0], terms[1]], # Z0 <--> Z0 Z1 + [terms[0], terms[2]], # Z0 <--> Z0 Z1 Z2 + [terms[0], terms[3]], # Z0 <--> Z0 Z1 Z2 Z3 + [terms[1], terms[2]], # Z0 Z1 <--> Z0 Z1 Z2 + [terms[2], terms[3]], # Z0 Z1 Z2 <--> Z0 Z1 Z2 Z3 + [terms[1], terms[3]], # Z0 Z1 <--> Z0 Z1 Z2 Z3 + [terms[0], terms[4]], # Z0 <--> X2 X3 + [terms[1], terms[4]], # Z0 Z1 <--> X2 X3 + [terms[4], terms[5]], # X2 X3 <--> Y0 X2 X3 + [terms[4], terms[6]], # X2 X3 <--> Y0 Y1 X2 X3 + [terms[5], terms[6]], # Y0 X2 X3 <--> Y0 Y1 X2 X3 + ]) + + plt.margins(x=0.1) + nx.draw( + G, + labels={node: format_pauli_word(node) for node in terms}, + with_labels=True, + node_size=500, + font_size=8, + node_color="#9eded1", + edge_color="#c1c1c1" + ) + + ############################################################################## + # We can now generate the complement graph (compare this to our handdrawn + # version above!): + + C = nx.complement(G) + coords = nx.spring_layout(C) + + nx.draw( + C, + coords, + labels={node: format_pauli_word(node) for node in terms}, + with_labels=True, + node_size=500, + font_size=8, + node_color="#9eded1", + edge_color="#c1c1c1" + ) + + + ############################################################################## + # Now that we have the complement graph, we can perform a greedy coloring to + # determine the minimum number of QWC groups: + + groups = nx.coloring.greedy_color(C, strategy="largest_first") + + # plot the complement graph with the greedy colouring + nx.draw( + C, + coords, + labels={node: format_pauli_word(node) for node in terms}, + with_labels=True, + node_size=500, + font_size=8, + node_color=[("#9eded1", "#aad4f0")[groups[node]] for node in C], + edge_color="#c1c1c1" + ) num_groups = len(set(groups.values())) @@ -743,8 +752,8 @@ def cost_fn(weights): # how this affects the number of measurements required to perform the VQE on :math:`\text{H}_2 \text{O}`! # Let's use our new-found knowledge to see what happens. -symbols, coordinates = qml.qchem.read_structure("h2o.xyz") -H, num_qubits = qml.qchem.molecular_hamiltonian(symbols, coordinates) +dataset = qml.data.load('qchem', molname="H2O")[0] +H, num_qubits = dataset.hamiltonian, len(dataset.hamiltonian.wires) print("Number of Hamiltonian terms/required measurements:", len(H.ops)) # grouping @@ -752,8 +761,8 @@ def cost_fn(weights): print("Number of required measurements after optimization:", len(groups)) ############################################################################## -# We went from 2110 required measurements/circuit evaluations to 556 (just over *two thousand* -# down to *five hundred* 😱😱😱). +# We went from 1086 required measurements/circuit evaluations to 320 (just over *one thousand* +# down to *three hundred* 😱😱😱). # # As impressive as this is, however, this is just the beginning of the optimization. # @@ -775,6 +784,12 @@ def cost_fn(weights): # So the next time you are working on a variational quantum algorithm and the number # of measurements required begins to explode—stop, take a deep breath 😤, and consider grouping # and optimizing your measurements. +# +# .. note:: +# +# Qubit-wise commuting group information for a wide variety of molecules has been +# pre-computed, and is available for download in +# in the `PennyLane Datasets library `__. ############################################################################## # References diff --git a/demonstrations/tutorial_ml_classical_shadows.py b/demonstrations/tutorial_ml_classical_shadows.py index 761b1a063f..c64c6ca7c8 100644 --- a/demonstrations/tutorial_ml_classical_shadows.py +++ b/demonstrations/tutorial_ml_classical_shadows.py @@ -181,7 +181,7 @@ def corr_function(i, j): def circuit(psi, observables): psi = psi / np.linalg.norm(psi) # normalize the state - qml.QubitStateVector(psi, wires=range(num_qubits)) + qml.StatePrep(psi, wires=range(num_qubits)) return [qml.expval(o) for o in observables] circuit_exact = qml.QNode(circuit, dev_exact) diff --git a/demonstrations/tutorial_pasqal.py b/demonstrations/tutorial_pasqal.py index f4d3efce47..a4bc63ba5b 100644 --- a/demonstrations/tutorial_pasqal.py +++ b/demonstrations/tutorial_pasqal.py @@ -323,7 +323,7 @@ def cost(): opt = tf.keras.optimizers.Adam(learning_rate=0.1) for step in range(100): - opt.minimize(cost, weights) + opt.minimize(cost, [weights]) if step % 5 == 0: print("Step {}: cost={}".format(step, cost())) diff --git a/demonstrations/tutorial_photonics.py b/demonstrations/tutorial_photonics.py index 61fc2429a8..c31667b9d1 100644 --- a/demonstrations/tutorial_photonics.py +++ b/demonstrations/tutorial_photonics.py @@ -159,12 +159,12 @@ @qml.qnode(dev) def vacuum_measure_x(): - return qml.sample(qml.X(0)) # Samples X quadratures + return qml.sample(qml.QuadX(0)) # Samples X quadratures @qml.qnode(dev) def vacuum_measure_p(): - return qml.sample(qml.P(0)) # Samples P quadrature + return qml.sample(qml.QuadP(0)) # Samples P quadrature # Sample measurements in phase space @@ -203,22 +203,22 @@ def vacuum_measure_p(): @qml.qnode(dev_exact) def vacuum_mean_x(): - return qml.expval(qml.X(0)) # Returns exact expecation value of x + return qml.expval(qml.QuadX(0)) # Returns exact expecation value of x @qml.qnode(dev_exact) def vacuum_mean_p(): - return qml.expval(qml.P(0)) # Returns exact expectation value of p + return qml.expval(qml.QuadP(0)) # Returns exact expectation value of p @qml.qnode(dev_exact) def vacuum_var_x(): - return qml.var(qml.X(0)) # Returns exact variance of x + return qml.var(qml.QuadX(0)) # Returns exact variance of x @qml.qnode(dev_exact) def vacuum_var_p(): - return qml.var(qml.P(0)) # Returns exact variance of p + return qml.var(qml.QuadP(0)) # Returns exact variance of p # Print calculated statistical quantities @@ -277,13 +277,13 @@ def vacuum_var_p(): @qml.qnode(dev) def measure_coherent_x(alpha, phi): qml.CoherentState(alpha, phi, wires=0) # Prepares coherent state - return qml.sample(qml.X(0)) # Measures X quadrature + return qml.sample(qml.QuadX(0)) # Measures X quadrature @qml.qnode(dev) def measure_coherent_p(alpha, phi): qml.CoherentState(alpha, phi, wires=0) # Prepares coherent state - return qml.sample(qml.P(0)) # Measures P quadrature + return qml.sample(qml.QuadP(0)) # Measures P quadrature # Choose alpha and phi and sample 1000 measurements @@ -360,14 +360,14 @@ def measure_coherent_p(alpha, phi): def displace_coherent_x(alpha, phi, x): qml.CoherentState(alpha, phi, wires = 0) # Create coherent state qml.Displacement(x, 0, wires = 0) # Second argument is the displacement direction in phase space - return qml.sample(qml.X(0)) + return qml.sample(qml.QuadX(0)) @qml.qnode(dev) def displace_coherent_p(alpha, phi, x): qml.CoherentState(alpha, phi, wires = 0) qml.Displacement(x, 0, wires = 0) - return qml.sample(qml.P(0)) + return qml.sample(qml.QuadP(0)) # We plot both the initial and displaced state @@ -429,7 +429,7 @@ def disp_optics(z, x): qml.CoherentState(z, 0, wires = 0) # High-amplitude auxiliary coherent state qml.CoherentState(3, np.pi / 3, wires = 1) # Input state (e.g. low amplitude coherent state) qml.Beamsplitter(np.arccos(1 - x ** 2 / z ** 2), 0, wires=[0, 1]) # Beamsplitter - return qml.sample(qml.X(1)) # Measure x quadrature + return qml.sample(qml.QuadX(1)) # Measure x quadrature @qml.qnode(dev2) @@ -437,7 +437,7 @@ def mom_optics(z, x): qml.CoherentState(z, 0, wires = 0) qml.CoherentState(3, np.pi / 3, wires = 1) qml.Beamsplitter(np.arccos(1 - x ** 2 / z ** 2), 0, wires = [0, 1]) - return qml.sample(qml.P(1)) # Measure p quadrature + return qml.sample(qml.QuadP(1)) # Measure p quadrature # Plot quadrature measurement before and after implementation of displacement @@ -504,13 +504,13 @@ def mom_optics(z, x): @qml.qnode(dev) def measure_squeezed_x(r): qml.Squeezing(r, 0, wires = 0) - return qml.sample(qml.X(0)) + return qml.sample(qml.QuadX(0)) @qml.qnode(dev) def measure_squeezed_p(r): qml.Squeezing(r, 0, wires = 0) - return qml.sample(qml.P(0)) + return qml.sample(qml.QuadP(0)) # Choose alpha and phi and sample 1000 measurements @@ -613,7 +613,7 @@ def measure_n_coherent(alpha, phi): @qml.qnode(dev_exact2) def measurement(a, phi): qml.Displacement(a, phi, wires = 0) # Implement displacement using PennyLane - return qml.expval(qml.X(0)) + return qml.expval(qml.QuadX(0)) @qml.qnode(dev_exact2) diff --git a/demonstrations/tutorial_qgrnn.py b/demonstrations/tutorial_qgrnn.py index a88df5799d..7b62d82338 100644 --- a/demonstrations/tutorial_qgrnn.py +++ b/demonstrations/tutorial_qgrnn.py @@ -485,7 +485,7 @@ def swap_test(control, register1, register2): def qgrnn(weights, bias, time=None): # Prepares the low energy state in the two registers - qml.QubitStateVector(np.kron(low_energy_state, low_energy_state), wires=reg1 + reg2) + qml.StatePrep(np.kron(low_energy_state, low_energy_state), wires=reg1 + reg2) # Evolves the first qubit register with the time-evolution circuit to # prepare a piece of quantum data diff --git a/demonstrations/tutorial_quantum_chemistry.py b/demonstrations/tutorial_quantum_chemistry.py index 96f8b76a4d..bcc58bde32 100644 --- a/demonstrations/tutorial_quantum_chemistry.py +++ b/demonstrations/tutorial_quantum_chemistry.py @@ -11,7 +11,12 @@ .. related:: tutorial_vqe A brief overview of VQE -*Author: Alain Delgado — Posted: 02 August 2020. Last updated: 25 June 2022.* +*Author: Alain Delgado — Posted: 02 August 2020. Last updated: 29 August 2023.* + +.. note:: + + A wide variety of molecular data, including Hamiltonians, is + available on the `PennyLane Datasets service `__. The ultimate goal of computational quantum chemistry is to unravel the quantum effects that determine the structure and properties of molecules. Reaching diff --git a/demonstrations/tutorial_sc_qubits.py b/demonstrations/tutorial_sc_qubits.py index 7050e96844..1c1c41899e 100644 --- a/demonstrations/tutorial_sc_qubits.py +++ b/demonstrations/tutorial_sc_qubits.py @@ -420,14 +420,14 @@ def measure_P_shots(time, state): qml.Displacement(epsilon * time, 0, wires=0) qml.Rotation((-1) ** state * chi * time, wires=0) - return qml.sample(qml.P(0)) + return qml.sample(qml.QuadP(0)) @qml.qnode(dev, interface="autograd") def measure_X_shots(time, state): qml.Displacement(epsilon * time, 0, wires=0) qml.Rotation((-1) ** state * chi * time, wires=0) - return qml.sample(qml.X(0)) + return qml.sample(qml.QuadX(0)) ############################################################################## diff --git a/demonstrations/tutorial_variational_classifier.py b/demonstrations/tutorial_variational_classifier.py index 411a8bcc0f..a58c9f09b2 100644 --- a/demonstrations/tutorial_variational_classifier.py +++ b/demonstrations/tutorial_variational_classifier.py @@ -336,7 +336,7 @@ def test(angles): ############################################################################## # Note that the ``default.qubit`` simulator provides a shortcut to # ``statepreparation`` with the command -# ``qml.QubitStateVector(x, wires=[0, 1])``. However, some devices may not +# ``qml.StatePrep(x, wires=[0, 1])``. However, some devices may not # support an arbitrary state-preparation routine. # # Since we are working with only 2 qubits now, we need to update the layer diff --git a/demonstrations/tutorial_vqe.py b/demonstrations/tutorial_vqe.py index 3e53b5964c..a0c27616ac 100644 --- a/demonstrations/tutorial_vqe.py +++ b/demonstrations/tutorial_vqe.py @@ -15,7 +15,7 @@ tutorial_vqe_spin_sectors VQE in different spin sectors tutorial_vqt Variational quantum thermalizer -*Author: Alain Delgado — Posted: 08 February 2020. Last updated: 25 June 2022.* +*Author: Alain Delgado — Posted: 08 February 2020. Last updated: 29 August 2023.* The Variational Quantum Eigensolver (VQE) is a flagship algorithm for quantum chemistry using near-term quantum computers [#peruzzo2014]_. It is an application of the @@ -79,6 +79,21 @@ # For a more comprehensive discussion on how to build the Hamiltonian of more # complicated molecules, see the tutorial :doc:`tutorial_quantum_chemistry`. # +# .. note:: +# +# A wide variety of molecular data, including Hamiltonians, is +# available on the `PennyLane Datasets service `__. +# This data can be downloaded using the :func:`~.pennylane.data.load` function: +# +# .. code-block:: python +# +# dataset = qml.data.load('qchem', molname="H2")[0] +# H, qubits = dataset.hamiltonian, len(dataset.hamiltonian.wires) +# +# For more details, check out the +# `Quantum Datasets `__ +# documentation. +# # Implementing the VQE algorithm # ------------------------------ # From here on, we can use PennyLane as usual, employing its entire stack of diff --git a/demonstrations/tutorial_vqe_qng.py b/demonstrations/tutorial_vqe_qng.py index 806a490372..bb84cde292 100644 --- a/demonstrations/tutorial_vqe_qng.py +++ b/demonstrations/tutorial_vqe_qng.py @@ -12,7 +12,7 @@ tutorial_vqe A brief overview of VQE tutorial_quantum_natural_gradient Quantum natural gradient -*Authors: Maggie Li, Lana Bozanic, Sukin Sim — Posted: 06 November 2020. Last updated: 08 April 2021.* +*Authors: Maggie Li, Lana Bozanic, Sukin Sim — Posted: 06 November 2020. Last updated: 29 August 2023.* This tutorial showcases how one can apply quantum natural gradients (QNG) [#stokes2019]_ [#yamamoto2019]_ to accelerate the optimization step of the Variational Quantum Eigensolver (VQE) algorithm [#peruzzo2014]_. @@ -258,16 +258,10 @@ def cost_fn(params): # Hydrogen VQE Example # -------------------- # -# To construct our system Hamiltonian, we first read the molecular geometry from -# the external file :download:`h2.xyz ` using the -# :func:`~.pennylane.qchem.read_structure` function (see more details in the -# :doc:`tutorial_quantum_chemistry` tutorial). The molecular Hamiltonian is then -# built using the :func:`~.pennylane.qchem.molecular_hamiltonian` function. +# To construct our system Hamiltonian, we can use `PennyLane Datasets `__ to obtain the dataset for a :math:`\text{H}_2` molecule. -geo_file = "h2.xyz" - -symbols, coordinates = qml.qchem.read_structure(geo_file) -hamiltonian, qubits = qml.qchem.molecular_hamiltonian(symbols, coordinates) +dataset = qml.data.load('qchem',molname="H2", bondlength=0.7)[0] +hamiltonian, qubits = dataset.hamiltonian, len(dataset.hamiltonian.wires) print("Number of qubits = ", qubits) @@ -304,9 +298,9 @@ def cost(params): ############################################################################## # For this problem, we can compute the exact value of the -# ground state energy via exact diagonalization. We provide the value below. +# ground state energy via exact diagonalization. We provide the value below using the dataset. -exact_value = -1.136189454088 +exact_value = dataset.fci_energy # -1.1361895496530567 ############################################################################## diff --git a/demonstrations/vqe_parallel.py b/demonstrations/vqe_parallel.py index fded430509..29673d2f29 100644 --- a/demonstrations/vqe_parallel.py +++ b/demonstrations/vqe_parallel.py @@ -12,14 +12,13 @@ tutorial_vqe A brief overview of VQE -*Author: Tom Bromley — Posted: 14 February 2020. Last updated: 9 November 2022.* +*Author: Tom Bromley — Posted: 14 February 2020. Last updated: 29 August 2023.* This tutorial showcases how using asynchronously-evaluated parallel QPUs can speed up the calculation of the potential energy surface of molecular hydrogen (:math:`H_2`). Using a VQE setup, we task two devices from the `PennyLane-Rigetti `__ plugin with evaluating - separate terms in the qubit Hamiltonian of :math:`H_2`. As these devices are allowed to operate asynchronously, i.e., at the same time and without having to wait for each other, the calculation can be performed in roughly half the time. @@ -52,44 +51,23 @@ # hydrogen. This is achieved by finding the ground state energy of :math:`H_{2}` as we increase # the bond length between the hydrogen atoms. # -# Each inter-atomic distance results in a different qubit Hamiltonian. To find the corresponding -# Hamiltonian, we use the :func:`~.pennylane.qchem.molecular_hamiltonian` function of the -# :mod:`~.pennylane.qchem` package. Further details on the mapping from the electronic -# Hamiltonian of a molecule to a qubit Hamiltonian can be found in the +# Each inter-atomic distance results in a different qubit Hamiltonian. Further +# details on the mapping from the electronic Hamiltonian of a molecule to a +# qubit Hamiltonian can be found in the # :doc:`tutorial_quantum_chemistry` and :doc:`tutorial_vqe` # tutorials. # -# We begin by creating a dictionary containing a selection of bond lengths and corresponding data -# files saved in `XYZ `__ format. These files -# follow a standard format for specifying the geometry of a molecule and can be downloaded as a -# Zip from :download:`here <../demonstrations/vqe_parallel/vqe_parallel.zip>`. - -data = { # keys: atomic separations (in Angstroms), values: corresponding files - 0.3: "vqe_parallel/h2_0.30.xyz", - 0.5: "vqe_parallel/h2_0.50.xyz", - 0.7: "vqe_parallel/h2_0.70.xyz", - 0.9: "vqe_parallel/h2_0.90.xyz", - 1.1: "vqe_parallel/h2_1.10.xyz", - 1.3: "vqe_parallel/h2_1.30.xyz", - 1.5: "vqe_parallel/h2_1.50.xyz", - 1.7: "vqe_parallel/h2_1.70.xyz", - 1.9: "vqe_parallel/h2_1.90.xyz", - 2.1: "vqe_parallel/h2_2.10.xyz", -} - -############################################################################## -# The next step is to create the qubit Hamiltonians for each value of the inter-atomic distance. -# We do this by first reading the molecular geometry from the external file using the -# :func:`~.pennylane.qchem.read_structure` function and passing the atomic symbols -# and coordinates to :func:`~.pennylane.qchem.molecular_hamiltonian`. +# We begin by downloading a selection of datasets of :math:`H_2` molecule for +# various bond lengths using the +# `PennyLane Datasets library `__: +bonds = [0.5, 0.58, 0.7, 0.9, 1.1, 1.3, 1.5, 1.7, 1.9, 2.1] +datasets = qml.data.load("qchem", molname="H2", bondlength=bonds, basis="STO-3G") -hamiltonians = [] +############################################################################## +# We can now extract the qubit Hamiltonians from these datasets for each bond length: -for separation, file in data.items(): - symbols, coordinates = qchem.read_structure(file) - h = qchem.molecular_hamiltonian(symbols, coordinates, name=str(separation))[0] - hamiltonians.append(h) +hamiltonians = [d.hamiltonian for d in datasets] ############################################################################## # Each Hamiltonian can be written as a linear combination of fifteen tensor products of Pauli @@ -380,4 +358,4 @@ def compute_energy_parallel_optimized(H, devs, param): ############################################################################## # About the author # ---------------- -# .. include:: ../_static/authors/thomas_bromley.txt \ No newline at end of file +# .. include:: ../_static/authors/thomas_bromley.txt diff --git a/poetry.lock b/poetry.lock index d41c3f5801..6aeec9f297 100644 --- a/poetry.lock +++ b/poetry.lock @@ -1,4 +1,4 @@ -# This file is automatically @generated by Poetry 1.5.1 and should not be changed by hand. +# This file is automatically @generated by Poetry 1.6.1 and should not be changed by hand. 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(>=0.3.7)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-ignore-flaky", "pytest-mypy (>=0.9.1)", "pytest-ruff"] [[package]] name = "zstd" @@ -5689,4 +5620,4 @@ files = [ [metadata] lock-version = "2.0" python-versions = "~3.9.0" -content-hash = "4112fc8e0dc6a99912c25993fdda88e52257a9755a2119a95ce6bc2a2b4418bf" +content-hash = "e7c0b8b8734a889c218b03120f9a122171286d0d809330a2ae4c8fe93c601fd8" diff --git a/pyproject.toml b/pyproject.toml index ac207e65f1..7b470fef1f 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -23,16 +23,14 @@ python = "~3.9.0" # Base dependencies needed to build the website without any code execution (*-norun) [tool.poetry.group.base.dependencies] -sphinx = "3.5.3" -sphinx-sitemap = "*" -sphinx_gallery = "0.10.0" +sphinx = "3.5.4" +sphinx_gallery = "0.12.2" Jinja2 = "2.11.3" markupsafe = "2.0.1" numpy = "~1.23" pyyaml = "^6.0.1" pennylane-sphinx-theme = "*" pypandoc = "1.5" -docutils = ">=0.12,<0.17" # Sphinx breaks if >= 0.17 is installed [tool.poetry.group.executable-dependencies.dependencies] ########################################################### @@ -40,10 +38,10 @@ docutils = ">=0.12,<0.17" # Sphinx breaks if >= 0.17 is installed # These pinned versions of PL are NOT used for Dev builds # # The latest commit from GitHub is used instead # ########################################################### -pennylane = "0.31.0" -pennylane-cirq = "0.31.0" -pennylane-qiskit = "0.31.0" -pennylane-qulacs = "0.29.0" +pennylane = "0.32.0" +pennylane-cirq = "0.32.0" +pennylane-qiskit = "0.32.0" +pennylane-qulacs = "0.32.0" ########################################################## @@ -78,13 +76,13 @@ torchvision = [ ] # The following packages are platform locked to not install on MacOS as the installation breaks -scikit-learn = { version = "0.24.0", markers = "platform_machine == 'x86_64'" } -tensorflow = { version = "2.8.1", markers = "platform_machine == 'x86_64'" } +scikit-learn = { version = "1.3.0", markers = "platform_machine == 'x86_64'" } +tensorflow = { version = "2.13.0", markers = "platform_machine == 'x86_64'" } flamingpy = { version = ">=0.10.1b1", markers = "platform_machine == 'x86_64'" } qulacs = {version = "0.1.10.1", markers = "platform_machine == 'x86_64'" } # The following packages are only installed on MacOS for compatibility -tensorflow-macos = { version = "2.8.0", markers = "sys_platform == 'darwin' and platform_machine == 'arm64'" } +tensorflow-macos = { version = "2.13.0", markers = "sys_platform == 'darwin' and platform_machine == 'arm64'" } [tool.poetry.group.metadata-validation] optional = true From ff8fb0681a6d82da879761519ccd36bcb0ce3dd2 Mon Sep 17 00:00:00 2001 From: josephbowles <54283511+josephbowles@users.noreply.github.com> Date: Fri, 1 Sep 2023 21:41:46 +0200 Subject: [PATCH 09/24] Contextuality demo (#719) Demo: Contextuality and inductive bias in quantum machine learning This is a demo for my recent paper https://arxiv.org/abs/2302.01365 In the demo we study the toy learning problem described in the paper, and build and train a quantum model that encodes the relevant inductive bias. The model is shown to outperform a 'generic' quantum model that does not encode this bias. The demo is not focused much on contextuality (because it would require a lot of explanation), but rather focuses on the type of learning problem (inspired from contextuality) that is presented in the paper. JAX is used for vectorization and JIT compilation. --------- Co-authored-by: Ivana Kurecic Co-authored-by: Guillermo Alonso-Linaje <65235481+KetpuntoG@users.noreply.github.com> --- .../thumbnail_large_Contextuality.png | Bin 0 -> 122792 bytes .../contextuality/contextuality_thumbnail.png | Bin 0 -> 46352 bytes demonstrations/contextuality/model.png | Bin 0 -> 161704 bytes demonstrations/contextuality/rps.png | Bin 0 -> 46352 bytes demonstrations/contextuality/rpstable.png | Bin 0 -> 125939 bytes .../socialthumbnail_large_Contextuality.png | Bin 0 -> 117022 bytes .../thumbnail_tutorial_Contextuality.png | Bin 0 -> 24699 bytes .../tutorial_contextuality.metadata.json | 83 ++ demonstrations/tutorial_contextuality.py | 805 ++++++++++++++++++ demos_qml.rst | 9 + 10 files changed, 897 insertions(+) create mode 100644 _static/large_demo_thumbnails/thumbnail_large_Contextuality.png create mode 100644 demonstrations/contextuality/contextuality_thumbnail.png create mode 100644 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"previewImages": [ + { + "type": "thumbnail", + "uri": "/_images/thumbnail_tutorial_Contextuality.png" + }, + { + "type": "large_thumbnail", + "uri": "/_static/large_demo_thumbnails/thumbnail_large_Contextuality.png" + } + ], + "seoDescription": "Train a problem-inspired ansatz on a contextuality-inspired dataset.", + "doi": "", + "canonicalURL": "/qml/demos/tutorial_contextuality", + "references": [ + { + "id": "paper", + "type": "article", + "title": "Contextuality and inductive bias in quantum machine learning.", + "authors": "J. Bowles, V. J. Wright, M. Farkas, N. Killoran, M. Schuld", + "year": "2023", + "journal": "", + "url": "https://arxiv.org/abs/2302.01365" + }, + { + "id": "contextuality", + "type": "article", + "title": "Contextuality for preparations, transformations, and unsharp measurements.", + "authors": "R. W. Spekkens", + "year": "2005", + "journal": "Phys. Rev. A 71", + "url": "https://journals.aps.org/pra/abstract/10.1103/PhysRevA.71.052108" + }, + { + "id": "reptheory", + "type": "article", + "title": "Representation Theory for Geometric Quantum Machine Learning.", + "authors": "M. Ragone, P. Braccia, Q. T. Nguyen, L. Schatzki, P. J. Coles, F. Sauvage, M. Larocca, M. Cerezo", + "year": "2023", + "journal": "", + "url": "https://arxiv.org/abs/2210.07980" + }, + { + "id": "equivariant", + "type": "article", + "title": "Theory for Equivariant Quantum Neural Networks.", + "authors": "Q. T. Nguyen, L. Schatzki, P. Braccia, M. Ragone, P. J. Coles, F. Sauvage, M. Larocca, M. Cerezo", + "year": "2022", + "journal": "", + "url": "https://arxiv.org/abs/2210.08566" + }, + { + "id": "surrogates", + "type": "article", + "title": "Classical surrogates for quantum learning models.", + "authors": "F. J. Schreiber, J. Eiser, J. J. Meyer", + "year": "2022", + "journal": "", + "url": "https://arxiv.org/abs/2206.11740" + } + ], + "basedOnPapers": [], + "referencedByPapers": [], + "relatedContent": [ + { + "type": "demonstration", + "id": "tutorial_geometric_qml", + "weight": 1.0 + } + ] +} \ No newline at end of file diff --git a/demonstrations/tutorial_contextuality.py b/demonstrations/tutorial_contextuality.py new file mode 100644 index 0000000000..842958be48 --- /dev/null +++ b/demonstrations/tutorial_contextuality.py @@ -0,0 +1,805 @@ +r""" +Contextuality and inductive bias in QML +============================================================ + +.. meta:: + :property="og:description": Train a tailored quantum model on a contextuality-inspired dataset + :property="og:image": https://pennylane.ai/qml/_images/contextuality_thumbnail.png + +.. related:: + tutorial_geometric_qml + +*Author: Joseph Bowles — Posted: 21 March 2023* + +""" + + +###################################################################### +# What machine learning problems are quantum computers likely to excel +# at? +# +# In the article *Contextuality and inductive bias in quantum machine +# learning* [#paper]_ by Joseph Bowles, +# Victoria J Wright, Máté Farkas, Nathan Killoran and Maria Schuld, we +# look to contextuality for answers to this question. +# +# Contextuality is a nonclassical phenomenon exhibited by quantum +# systems, and it is necessary for computational advantage relative to +# classical machines. To be a little more specific, we focus on the +# framework of *generalized +# contextuality* [#contextuality]_, +# which was introduced by Robert Spekkens in 2004. We find learning +# problems for which contextuality plays a key role, and these problems +# may therefore be good areas where quantum machine learning algorithms +# shine. In this demo we will: +# +# - Describe a specific example of a contextuality-relevant problem that is based on the +# well-known rock-paper-scissors game, and +# - Construct and train a quantum model that is tailored to the +# symmetries of the problem. +# +# Throughout the demo we will make use of JAX to vectorise and just-in-time compile +# certain functions, which will speed things up. For more information on how to +# combine JAX and PennyLane, see the PennyLane +# `documentation `__. +# +# .. figure:: ../demonstrations/contextuality/socialthumbnail_large_Contextuality.png +# :align: center +# :width: 50% +# + +###################################################################### +# Generalized contextuality +# ------------------------- +# + + +###################################################################### +# Suppose we want to prepare the maximally mixed state of a single qubit, +# with :math:`\rho = \frac{1}{2}\mathbb{I}`. Although this corresponds to a +# single density matrix, there are many ways we could prepare the state. +# For example, we could mix the states :math:`\vert 0 \rangle` or +# :math:`\vert 1 \rangle` with equal probability. Alternatively, we could +# use the :math:`X` basis, and mix the states :math:`\vert + \rangle` or +# :math:`\vert - \rangle`. Even though this may not strike us as particularly +# strange, a remarkable coincidence is in fact going on here: an +# experimentalist can perform two physically distinct procedures (namely, +# preparing :math:`\rho` in the :math:`Z` or :math:`X` basis), however it +# is impossible to distinguish which procedure was performed, since they +# both result in the same density matrix and therefore give identical +# predictions for all future measurements. +# +# Such a coincidence demands an explanation. Something that one might expect +# is the following: the description of the experiment in terms of quantum +# states is not the most fundamental, and there are in fact other states +# (we’ll write them as :math:`\lambda`), that comprise our quantum states. +# In contextuality these are called *ontic states*, although they also go +# by the name of *hidden variables*. When we prepare a state +# :math:`\vert 0 \rangle`, :math:`\vert 1 \rangle`, +# :math:`\vert + \rangle`, :math:`\vert - \rangle`, what is really going +# on is that we prepare a mixture :math:`P_{\vert 0 \rangle}(\lambda)`, +# :math:`P_{\vert 1 \rangle}(\lambda)`, +# :math:`P_{\vert + \rangle}(\lambda)`, +# :math:`P_{\vert - \rangle}(\lambda)` over the true ontic states. One may +# imagine that the corresponding mixtures over the :math:`\lambda` s are +# the same for the :math:`Z` and :math:`X` basis preparation: +# +# .. math:: \frac{1}{2}P_{\vert 0 \rangle}(\lambda)+\frac{1}{2}P_{\vert 1 \rangle}(\lambda)=\frac{1}{2}P_{\vert + \rangle}(\lambda)+\frac{1}{2}P_{\vert - \rangle}(\lambda). +# +# This is a rather natural explanation of our coincidence: the two +# procedures are indistinguishable because they actually correspond to the +# same mixture over the fundamental states :math:`\lambda`. This sort of +# explanation is called *non-contextual*, since the two mixtures do not +# depend on the basis (that is, the context) in which the state is +# prepared. It turns out that if one tries to apply this logic to all the +# indistinguishabilities in quantum theory, one arrives at contradictions: +# it simply cannot be done. For this reason we say that quantum theory is +# a *contextual* theory. +# +# In the paper we frame generalized contextuality in the machine learning +# setting, which allows us to define what we mean by a contextual learning +# model. In a nutshell, this definition demands that if a learning model +# is non-contextual, then any indistinguishabilities in the model should +# be explained in a non-contextual way similar to the above. This results +# in constraints on the learning model, which limits their expressivity. +# Since quantum models are contextual, they can of course go beyond these +# constraints, and understanding when and how they do this may shed light +# on the non-classical features that separate quantum models from +# classical ones. +# +# Below we describe a specific learning problem that demonstrates this +# approach. As we will see, the corresponding indistinguishability relates +# to an *inductive bias* of the learning model. +# + + +###################################################################### +# The rock-paper-scissors game +# ------------------------------ +# + + +###################################################################### +# The learning problem we will consider involves three players +# (we'll call them players 0, 1 and 2) playing a +# variant of the rock-paper-scissors game with a referee. +# The game goes as follows. In each round, a player can choose to play +# either rock (R), paper (P) or scissors (S). Each player also has a +# ‘special’ action. For player 0 it is R, for player 1 it is P and for +# player 2 it is S. The actions of the players are then compared pairwise, +# with the following rules: +# +# - If two players play different actions, then one player beats the +# other following the usual rule (rock beats scissors, scissors beats +# paper, paper beats rock). +# - If two players play the same action, the one who plays their special +# action beats the other. If neither plays their special action, it is +# a draw. +# +# A referee then decides the winners and the losers of that round: the +# winners receive :math:`\$1` and the losers lose :math:`\$1` (we will +# call this their *payoff* for that round). +# + +############################################################################## +# .. figure:: ../demonstrations/contextuality/rps.png +# :align: center +# :width: 50% + +###################################################################### +# Naturally, the more players a given player beats, the higher the +# probability that they get a positive payoff. In particular, if we denote +# the payoff of player :math:`k` by :math:`y_k=\pm1` then +# +# .. math:: \mathbb{E}(y_k) = \frac{n^k_{\text{win}}-n^k_{\text{lose}}}{2}, +# +# where :math:`n^k_{\text{win}}`, :math:`n^k_{\text{lose}}` is the number +# of players that player :math:`k` beats or loses to in that round. This +# ensures that a player is certain to get a positive (or negative) payoff +# if they beat (or lose) to everyone. +# +# To make this concrete, we will construct three 3x3 matrices ``A01``, +# ``A02``, ``A12`` which determine the rules for each pair of players. +# ``A01`` contains the expected payoff values of player 0 when playing +# against player 1. Using the rules of the game it looks as follows. +# + +############################################################################## +# .. figure:: ../demonstrations/contextuality/rpstable.png +# :align: center +# :width: 50% + + +###################################################################### +# The matrices ``A02`` and ``A12`` are defined similarly. +# + +import jax +import jax.numpy as jnp +import pennylane as qml +import numpy as np +jax.config.update("jax_platform_name", "cpu") +np.random.seed(666) # seed used for random functions + +A01 = np.array([[1, -1, 1], [1, -1, -1], [-1, 1, 0]]) # rules for player 0 vs player 1 +A02 = np.array([[1, -1, 1], [1, 0, -1], [-1, 1, -1]]) +A12 = np.array([[0, -1, 1], [1, 1, -1], [-1, 1, -1]]) + + +###################################################################### +# We can also define the matrices ``A10``, ``A20``, ``A21``. Since +# switching the players corresponds to taking the transpose matrix and +# a positive payoff for one player implies a negative for the other, +# these matrices are given by +# the negative of the transposed matrix: +# + +A10 = -A01.T # rules for player 1 vs player 0 +A20 = -A02.T +A21 = -A12.T + +###################################################################### +# Note that the above game is an example of a *zero-sum game*: if player 1 beats +# player 2 then necessarily player 2 loses to player 1. This implies +# :math:`\sum_k n^k_{\text{wins}}=\sum_kn^k_{\text{lose}}` and so in every +# round we have +# +# .. math:: \mathbb{E}(y_1)+\mathbb{E}(y_2)+\mathbb{E}(y_3)=0. +# + + +###################################################################### +# Constructing the dataset +# ------------ +# + + +###################################################################### +# Here we construct a dataset based on the above game. Our data points +# correspond to probability +# distributions over possible actions: in the zero-sum game literature +# these are called *strategies*. +# For example, a strategy for player k is a +# vector +# +# .. math:: x_k=(P(a_k=R), P(a_k=P), P(a_k=S)) +# +# where :math:`a_k` denotes player :math:`k`\ ’s action. We collect these +# into a strategy matrix X +# +# .. math:: +# +# X = \begin{pmatrix} +# P(a_0=R) & P(a_0=P) & P(a_0=S) \\ +# P(a_1=R) & P(a_1=P) & P(a_1=S) \\ +# P(a_2=R) & P(a_2=P) & P(a_2=S) . +# \end{pmatrix} +# +# +# + + +###################################################################### +# Let’s write a function +# to generate a set of strategy matrices. +# + + +def get_strategy_matrices(N): + """ + Generates N strategy matrices, normalised by row + """ + X = np.random.rand(N, 3, 3) + for i in range(N): + norm = np.array(X[i].sum(axis=1)) + for k in range(3): + X[i, k, :] = X[i, k, :] / norm[k] + return X + + +###################################################################### +# The labels in our dataset correspond to payoff values :math:`y_k` of the +# three players. Following the rules of probability we find that if the +# players use strategies :math:`x_0, x_1, x_2` the expected values of +# :math:`n_{\text{wins}}^k - n_{\text{lose}}^k` are given +# by +# +# .. math:: \mathbb{E}[n_{\text{wins}}^0 - n_{\text{lose}}^0] = x_0 \cdot A_{01}\cdot x_1^T+x_0 \cdot A_{02}\cdot x_2^T +# +# .. math:: \mathbb{E}[n_{\text{wins}}^1 - n_{\text{lose}}^1] = x_1 \cdot A_{10}\cdot x_0^T+x_1 \cdot A_{12}\cdot x_2^T +# +# .. math:: \mathbb{E}[n_{\text{wins}}^2 - n_{\text{lose}}^2] = x_2 \cdot A_{20}\cdot x_0^T+x_2 \cdot A_{21}\cdot x_1^T +# +# Since we have seen that +# :math:`\mathbb{E}(y_k) = \frac{n^k_{\text{win}}-n^k_{\text{lose}}}{2}` +# it follows that the probability for player :math:`k` to receive a +# positive payoff given strategies :math:`X` is +# +# .. math:: P(y_k=+1\vert X) = \frac{\mathbb{E}(y_k\vert X)+1}{2} = \frac{(\mathbb{E}[n_{\text{wins}}^k - n_{\text{lose}}^k])/2+1}{2} +# +# Putting all this together we can write some code to generate the labels +# for our data set. +# + + +def payoff_probs(X): + """ + get the payoff probabilities for each player given a strategy matrix X + """ + n0 = X[0] @ A01 @ X[1] + X[0] @ A02 @ X[2] # n0 = + n1 = X[1] @ A10 @ X[0] + X[1] @ A12 @ X[2] + n2 = X[2] @ A20 @ X[0] + X[2] @ A21 @ X[1] + probs = (jnp.array([n0, n1, n2]) / 2 + 1) / 2 + return probs + + +# JAX vectorisation +vpayoff_probs = jax.vmap(payoff_probs) + + +def generate_data(N): + X = get_strategy_matrices(N) # strategies + P = vpayoff_probs(X) # payoff probabilities + r = np.random.rand(*P.shape) + Y = np.where(P > r, 1, -1) # sampled payoffs for data labels + return X, Y, P + + +X, Y, P = generate_data(2000) + +print(X[0]) # the first strategy matrix in our dataset +print(Y[0]) # the corresponding sampled payoff values + +###################################################################### +# Note that since strategies are probabilistic mixtures of actions, our +# data labels satisfy a zero-sum condition +# +# .. math:: \mathbb{E}(y_1\vert X_i)+\mathbb{E}(y_2\vert X_i)+\mathbb{E}(y_3\vert X_i)=0. +# +# We can verify this using the payoff probability matrix ``P`` that we +# used to sample the labels: +# + +expvals = 2 * P - 1 # convert probs to expvals +expvals[:10].sum(axis=1) # check first 10 entries + + +###################################################################### +# The learning problem +# -------------------- +# + + +###################################################################### +# Suppose we are given a data set :math:`\{X_i,\vec{y}_i\}` consisting of +# strategy matrices and payoff values, however we don’t know what the +# underlying game is (that is, we don’t know the players were playing the +# rock, paper scissors game described above). We do have one piece of +# information though: we know the game is zero-sum so that the data +# generation process satisfies +# +# .. math:: \mathbb{E}(y_0\vert X_i)+\mathbb{E}(y_1\vert X_i)+\mathbb{E}(y_2\vert X_i)=0. +# +# Can we learn the rock, paper scissors game from this data? More +# precisely, if we are given an unseen strategy matrix +# :math:`X_{\text{test}}` our task is to sample from the three +# distributions +# +# .. math:: P(y_0\vert X_{\text{test}}), P(y_1\vert X_{\text{test}}), P(y_2\vert X_{\text{test}}). +# +# Note we are not asking to sample from the joint distribution +# :math:`P(\vec{y}\vert X_{\text{test}})` but the three marginal +# distributions only. This can be seen as an instance of multi-task +# learning, where a single task corresponds to sampling the payoff for one +# of the three players. +# + + +###################################################################### +# Building inductive bias into a quantum model +# -------------------------------------------- +# + + +###################################################################### +# Here we describe a simple three qubit model to tackle this problem. +# Since we know that the data satisfies the zero-sum condition, we aim to +# create a quantum model that encodes this knowledge. That is, like +# the data we want our model to satisfy +# +# .. math:: \mathbb{E}(y_0\vert X_i)+\mathbb{E}(y_1\vert X_i)+\mathbb{E}(y_2\vert X_i)=0. +# +# In machine learning, this is called encoding an *inductive +# bias* into the model, and considerations like this are often crucial for +# good generalisation performance. +# +# .. note:: +# Since the above holds for all :math:`X_i`, it implies an +# indistinguishability of the model: if we look at one of the labels at +# random, we are equally likely to see a positive or negative payoff +# regardless of :math:`X_i`, and so the :math:`X_i` are indistinguishable +# with respect to this observation. This implies a corresponding constraint +# on non-contextual learning models, which limits their expressivity and +# may therefore hinder their performance: see the paper for more details +# on how this looks in practice. Luckily for us quantum theory is a +# contextual theory, so these limitations don’t apply to our model! +# +# The quantum model we consider has the following structure: +# + +############################################################################## +# .. figure:: ../demonstrations/contextuality/model.png +# :align: center +# :width: 50% + + +###################################################################### +# The parameters :math:`\theta` and :math:`\alpha` are trainable +# parameters of the model, and we will use the three :math:`Z` +# measurements at the end of the circuit to sample the three labels. +# Therefore, if we write the entire circuit as +# :math:`\vert \psi(\alpha,\theta,X)\rangle` the zero sum condition will +# be satisfied if +# +# .. math:: \langle \psi(\alpha,\theta,X) \vert (Z_0+Z_1+Z_2) \vert \psi(\alpha,\theta,X) \rangle = 0. +# +# Let’s see how we can create a model class that satisfies this. For +# precise details on the structure of the model, check out Figure 6 in the +# paper. We’ll first look at the parameterised unitary :math:`V_{\alpha}`, +# that we call the *input preparation unitary*. This prepares a state +# :math:`V_\alpha\vert 0 \rangle` such that +# +# .. math:: \langle 0 \vert V^\dagger_\alpha (Z_0+Z_1+Z_2) V_\alpha\vert 0 \rangle = 0. +# +# An example of such a circuit is the following. +# + + +def input_prep(alpha): + # This ensures the prepared state has =0 + qml.Hadamard(wires=0) + qml.Hadamard(wires=1) + qml.Hadamard(wires=2) + qml.RY(alpha[0], wires=0) + qml.RY(alpha[0] + np.pi, wires=1) + + +###################################################################### +# The second unitary is a *bias invariant layer*: it preserves the value +# of :math:`\langle Z_0+Z_1+Z_2 \rangle` for all input states into the +# layer. To achieve this, the generators of the unitaries in this layer +# must commute with the operator :math:`Z_0+Z_1+Z_2`. For example the +# operator :math:`X\otimes X + Y\otimes Y + Z\otimes Z` (on any pair of +# qubits) commutes with :math:`Z_0+Z_1+Z_2` and so a valid parameterised +# gate could be +# +# .. math:: e^{i\theta(X\otimes X\otimes\mathbb{I} + Y\otimes Y\otimes\mathbb{I} + Z\otimes Z\otimes\mathbb{I})}. +# +# This kind of reasoning is an example of geometric quantum machine +# learning (check out [#reptheory]_ and [#equivariant]_ or our own +# `demo `__ for an awesome introduction to the subject). +# Below we construct the +# bias invariant layer: note that all the generators commute with +# :math:`Z_0+Z_1+Z_2`. The variables ``blocks`` and ``layers`` are model +# hyperparameters that we will fix as ``blocks=1`` and ``layers=2``. +# + +blocks = 1 +layers = 2 + + +def swap_rot(weights, wires): + """ + bias-invariant unitary with swap matrix as generator + """ + qml.PauliRot(weights, "XX", wires=wires) + qml.PauliRot(weights, "YY", wires=wires) + qml.PauliRot(weights, "ZZ", wires=wires) + + +def param_unitary(weights): + """ + A bias-invariant unitary (U in the paper) + """ + for b in range(blocks): + for q in range(3): + qml.RZ(weights[b, q], wires=q) + qml.PauliRot(weights[b, 3], "ZZ", wires=[0, 1]) + qml.PauliRot(weights[b, 4], "ZZ", wires=[0, 2]) + qml.PauliRot(weights[b, 5], "ZZ", wires=[1, 2]) + swap_rot(weights[b, 6], wires=[0, 1]) + swap_rot(weights[b, 7], wires=[1, 2]) + swap_rot(weights[b, 8], wires=[0, 2]) + + +def data_encoding(x): + """ + S_x^1 in paper + """ + for q in range(3): + qml.RZ(x[q], wires=q) + + +def data_encoding_pairs(x): + """ + S_x^2 in paper + """ + qml.PauliRot(x[0] * x[1], "ZZ", wires=[0, 1]) + qml.PauliRot(x[1] * x[2], "ZZ", wires=[1, 2]) + qml.PauliRot(x[0] * x[2], "ZZ", wires=[0, 2]) + + +def bias_inv_layer(weights, x): + """ + The full bias invariant layer. + """ + # data preprocessing + x1 = jnp.array([x[0, 0], x[1, 1], x[2, 2]]) + x2 = jnp.array(([x[0, 1] - x[0, 2], x[1, 2] - x[1, 0], x[2, 0] - x[2, 1]])) + for l in range(0, 2 * layers, 2): + param_unitary(weights[l]) + data_encoding(x1) + param_unitary(weights[l + 1]) + data_encoding_pairs(x2) + param_unitary(weights[2 * layers]) + + +###################################################################### +# With our ``input_prep`` and ``bias_inv_layer`` functions we can now +# define our quantum model. +# + +dev = qml.device("default.qubit", wires=3) + + +@qml.qnode(dev, interface="jax") +def model(weights, x): + input_prep(weights[2 * layers + 1, 0]) # alpha is stored in the weights array + bias_inv_layer(weights, x) + return [qml.expval(qml.PauliZ(0)), qml.expval(qml.PauliZ(1)), qml.expval(qml.PauliZ(2))] + + +# jax vectorisation, we vectorise over the data input (the second argument) +vmodel = jax.vmap(model, (None, 0)) +vmodel = jax.jit(vmodel) + + +###################################################################### +# To investigate the effect of the encoded inductive bias, we will compare +# this model to a generic model with the same data encoding and similar +# number of parameters (46 vs 45 parameters). +# + + +def generic_layer(weights, x): + # data preprocessing + x1 = jnp.array([x[0, 0], x[1, 1], x[2, 2]]) + x2 = jnp.array(([x[0, 1] - x[0, 2], x[1, 2] - x[1, 0], x[2, 0] - x[2, 1]])) + for l in range(0, 2 * layers, 2): + qml.StronglyEntanglingLayers(weights[l], wires=range(3)) + data_encoding(x1) + qml.StronglyEntanglingLayers(weights[l + 1], wires=range(3)) + data_encoding_pairs(x2) + qml.StronglyEntanglingLayers(weights[2 * layers], wires=range(3)) + + +dev = qml.device("default.qubit", wires=3) + + +@qml.qnode(dev, interface="jax") +def generic_model(weights, x): + generic_layer(weights, x) + return [qml.expval(qml.PauliZ(0)), qml.expval(qml.PauliZ(1)), qml.expval(qml.PauliZ(2))] + + +vmodel_generic = jax.vmap(generic_model, (None, 0)) +vmodel_generic = jax.jit(vmodel_generic) + + +###################################################################### +# **Warning**: Since we are using JAX it is important that our ``model`` +# and ``generic model`` functions are functionally pure (read more +# `here `__). +# This means we cannot change the values of ``blocks`` or ``layers`` from +# hereon since these values have been cached for JIT compilation. +# + + +###################################################################### +# Training and evaluation +# ----------------------- +# + + +###################################################################### +# To train the model we will minimise the negative log likelihood of the +# labels given the data +# +# .. math:: \mathcal{L} = -\frac{1}{3\vert N \vert}\sum_{(X_i,\vec{y}_i)} \log(\mathcal{P}_0(y_i^{(0)}\vert X_i))+\log(\mathcal{P}_1(y_i^{(1)}\vert X_i))+\log(\mathcal{P}_2(y_i^{(2)}\vert X_i)) +# +# Here :math:`\mathcal{P}_k` is the probability distribution of the +# :math:`k` label from the model, :math:`y_i^{(k)}` is the kth element +# of the payoff vector :math:`\vec{y}_i` in the dataset, and :math:`N` is +# the size of the training dataset. We remark that +# training the negative log likelihood is in some sense cheating, since +# for large quantum circuits we don’t know how to estimate it efficiently. +# As generative modeling in QML progresses, we can hope however that +# scalable methods that approximate this type of training may appear. +# + + +def likelihood(weights, X, Y, model): + """ + The cost function. Returns the negative log likelihood + """ + expvals = jnp.array(model(weights, X)).T + probs = (1 + Y * expvals) / 2 # get the relevant probabilites + probs = jnp.log(probs) + llh = jnp.sum(probs) / len(X) / 3 + return -llh + + +###################################################################### +# For evaluation we will use the average KL divergence between the true +# data distribution and the model distribution +# +# .. math:: \mathbb{E}_{P^\text{data}(X)} \left[\frac{1}{3}\sum_{k=1}^{3} D_{\text{KL}}(P^\text{data}_k(y\vert X)\vert\vert \mathcal{P}_k(y\vert X)) \right]. +# +# To estimate this we sample a test set of strategies, calculate their +# payoff probabilities, and estimate the above expectation via the sample +# mean. +# + +N_test = 10000 +X_test = get_strategy_matrices(N_test) + +probs_test = np.zeros([N_test, 3, 2]) +probs_test[:, :, 0] = vpayoff_probs(X_test) # the true probabilities for the test set +probs_test[:, :, 1] = 1 - probs_test[:, :, 0] +probs_test = jnp.array(probs_test) + + +def kl_div(p, q): + """ + Get the KL divergence between two probability distribtuions + """ + p = jnp.vstack([p, jnp.ones(len(p)) * 10 ** (-8)]) # lower cutoff of prob values of 10e-8 + p = jnp.max(p, axis=0) + return jnp.sum(q * jnp.log(q / p)) # forward kl div + + +def kl_marginals(probs, probs_test): + """ + get the mean KL divergence of the three marginal distributions + (the square brackets above) + """ + kl = 0 + for t in range(3): + kl = kl + kl_div(probs[t, :], probs_test[t, :]) + return kl / 3 + + +# vectorise the kl_marginals function. Makes estimating the average KL diverence of a model faster. +vkl_marginals = jax.vmap(kl_marginals, (0, 0)) + + +def get_av_test_kl(model, weights, probs_test, X_test): + """ + returns the average KL divergence for a test set X_test. + """ + N_test = len(X_test) + probs = np.zeros(probs_test.shape) + expvals = jnp.array(model(weights, X_test)).T + for t in range(3): + probs[:, t, 0] = (1 + expvals[:, t]) / 2 + probs[:, t, 1] = (1 - expvals[:, t]) / 2 + return np.sum(vkl_marginals(probs, probs_test)) / N_test + + +###################################################################### +# To optimise the model we make use of the JAX optimization library optax. +# We will use the adam gradient descent optimizer. +# + +import optax +from tqdm import tqdm + + +def optimise_model(model, nstep, lr, weights): + plot = [[], [], []] + optimizer = optax.adam(lr) + opt_state = optimizer.init(weights) + steps = tqdm(range(nstep)) + for step in steps: + # use optax to update parameters + llh, grads = jax.value_and_grad(likelihood)(weights, X, Y, model) + updates, opt_state = optimizer.update(grads, opt_state, weights) + weights = optax.apply_updates(weights, updates) + + kl = get_av_test_kl(model, weights, probs_test, X_test) + steps.set_description( + "Current divergence: %s" % str(kl) + " :::: " + "Current likelihood: %s" % str(llh) + ) + plot[0].append(step) + plot[1].append(float(llh)) + plot[2].append(float(kl)) + return weights, llh, kl, plot + + +###################################################################### +# We are now ready to generate a data set and optimize our models! +# + +# generate data +N = 2000 # number of data points +X, Y, P = generate_data(N) + +nstep = 2000 # number of optimisation steps + +lr = 0.001 # initial learning rate +weights_model = np.random.rand(2 * layers + 2, blocks, 9) * 2 * np.pi +weights_generic = np.random.rand(2 * layers + 1, blocks, 3, 3) * 2 * np.pi + +# optimise the structured model +weights_model, llh, kl, plot_model = optimise_model(vmodel, nstep, lr, weights_model) +# optimise the generic model +weights_generic, llh, kl, plot_genereic = optimise_model(vmodel_generic, nstep, lr, weights_generic) + + +###################################################################### +# Let’s plot the average KL divergence and the negative log likelihood for +# both models. +# + +import matplotlib.pyplot as plt + +# subplots +fig, (ax1, ax2) = plt.subplots(nrows=2, ncols=1, figsize=(8, 10)) +fig.tight_layout(pad=10.0) + +# KL divergence +ax1.plot(plot_model[0], plot_model[2], color="red", label="biased model") +ax1.plot(plot_genereic[0], plot_genereic[2], color="blue", label="generic model") + +ax1.set_yscale("log") +ax1.set_ylabel("KL divergence (test)") +ax1.set_xlabel("training step") +ax1.legend() + +# negative log likelihood +ax2.plot(plot_model[0], plot_model[1], color="red") +ax2.plot(plot_genereic[0], plot_genereic[1], color="blue") + +ax2.set_yscale("log") +ax2.set_ylabel("Negative log likelihood (train)") +ax2.set_xlabel("training step") + +plt.show() + +###################################################################### +# We see that the model that encodes the inductive bias achieves both a +# lower training error and generalisation error, as can be expected. +# Incorporating knowledge about the data into the model design is +# generally a very good idea! +# + + +###################################################################### +# Conclusion +# ---------- +# + +###################################################################### +# In this demo we have constructed a dataset whose structure is +# connected to generalized contextuality, and have shown how to encode +# this structure as an inductive bias of a quantum model class. As is +# often the case, we saw that this approach outperforms a generic model +# class that does not take this knowledge into account. As a general rule, +# considerations like this should be at the front of one's mind when +# building a quantum model for a specific task. +# +# That is all for this demo. In our paper [#paper]_, it is also shown how models of +# this kind can perform better than classical surrogate +# models [#surrogates]_ at this specific task, +# which further strengthens the claim that the inductive bias of the +# quantum model is useful. For more information and to read more about the +# link between contextuality and QML, check out the full paper. +# +# +# References +# ---------- +# +# .. [#paper] +# +# J. Bowles, V. J. Wright, M. Farkas, N. Killoran, M. Schuld +# "Contextuality and inductive bias in quantum machine learning." +# `arXiv:2302.01365 `__, 2023. +# +# .. [#contextuality] +# +# R. W. Spekkens +# "Contextuality for preparations, transformations, and unsharp measurements." +# `Phys. Rev. A 71, 052108 `__, 2005. +# +# .. [#reptheory] +# +# M. Ragone, P. Braccia, Q. T. Nguyen, L. Schatzki, P. J. Coles, F. Sauvage, M. Larocca, M. Cerezo +# "Representation Theory for Geometric Quantum Machine Learning." +# `arXiv:2210.07980 `__, 2023. +# +# .. [#equivariant] +# +# Q. T. Nguyen, L. Schatzki, P. Braccia, M. Ragone, P. J. Coles, F. Sauvage, M. Larocca, M. Cerezo +# "Theory for Equivariant Quantum Neural Networks." +# `arXiv:2210.08566 `__, 2022. +# +# .. [#surrogates] +# +# F. J. Schreiber, J. Eiser, J. J. Meyer +# "Classical surrogates for quantum learning models." +# `arXiv:2206.11740 `__, 2022. +# +# +# About the author +# ---------------- +# .. include:: ../_static/authors/joseph_bowles.txt diff --git a/demos_qml.rst b/demos_qml.rst index e1c394711f..9f8a77ff68 100644 --- a/demos_qml.rst +++ b/demos_qml.rst @@ -146,11 +146,19 @@ IBM, Google, Rigetti, and many more—or on real hardware devices. :description: :doc:`demos/tutorial_univariate_qvr` :tags: covalent pytorch +.. gallery-item:: + :tooltip: Train a problem-inspired ansatz on a contextuality-inspired dataset + :figure: demonstrations/contextuality/contextuality_thumbnail.png + :description: :doc:`demos/tutorial_contextuality` + :tags: jax geometric qml inductive bias + + .. gallery-item:: :tooltip: See an example of a permutation equivariant graph embedding in PennyLane :figure: demonstrations/equivariant_graph_embedding/thumbnail_tutorial_equivariant_graph_embedding.png :description: :doc:`demos/tutorial_equivariant_graph_embedding` :tags: embedding geometric qml + :html:`

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