From 9bc33318a804ccb905046889d91c5d462dfa6d41 Mon Sep 17 00:00:00 2001 From: Mudit Pandey Date: Thu, 31 Aug 2023 17:26:27 -0400 Subject: [PATCH] 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 β€” Posted: 18 January 2021. Last updated: 8 April 2021.* +*Author: Josh Izaac β€” 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 explodeβ€”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 β€” Posted: 02 August 2020. Last updated: 25 June 2022.* +*Author: Alain Delgado β€” 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 β€” Posted: 08 February 2020. Last updated: 25 June 2022.* +*Author: Alain Delgado β€” 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 β€” Posted: 06 November 2020. Last updated: 08 April 2021.* +*Authors: Maggie Li, Lana Bozanic, Sukin Sim β€” 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 β€” Posted: 14 February 2020. Last updated: 9 November 2022.* +*Author: Tom Bromley β€” 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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