From 9bc33318a804ccb905046889d91c5d462dfa6d41 Mon Sep 17 00:00:00 2001 From: Mudit Pandey Date: Thu, 31 Aug 2023 17:26:27 -0400 Subject: [PATCH 1/4] 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 2/4] 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:`

` @@ -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 3/4] 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 4/4] 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