diff --git a/Makefile b/Makefile index edfc64ec39..9cfcd7774d 100644 --- a/Makefile +++ b/Makefile @@ -62,6 +62,7 @@ download: wget --no-verbose -N https://download.pytorch.org/tutorial/hymenoptera_data.zip -P $(DATADIR) unzip -q -o $(DATADIR)/hymenoptera_data.zip -d $(DATADIR)/ +# TODO: Update flax, or certain tutorials e.g. tutorial_rl_pulse.py will fail due to implicit confliction between jax==0.6.0 and flax==0.9.0 environment: @command -v $(POETRY_BIN) --version >/dev/null 2>&1 || { echo >&2 "Setting up the environment requires a valid installation of python-poetry. Please install and add poetry to PATH or pass the executable using POETRY_BIN. Aborting."; exit 1; } @if [ '$(BASE_ONLY)' = 'true' ]; then\ diff --git a/README.md b/README.md index fd8949c188..23cb18c31f 100644 --- a/README.md +++ b/README.md @@ -165,7 +165,10 @@ Due to the large scope of requirements in this repository, the traditional `requ and `pyproject.toml` is being introduced instead, the goal being easier management in regard to adding/updating packages. To install all the dependencies locally, [poetry](https://python-poetry.org/) needs to be installed. Please follow the -[official installation documentation](https://python-poetry.org/docs/#installation). +[official installation documentation](https://python-poetry.org/docs/#installation). To ensure that no issues arise, we +recommend using the [official installer](https://python-poetry.org/docs/#installing-with-the-official-installer). + +**IMPORTANT NOTE:** It is important to install Poetry using the official instructions and NOT into the same virtual env as QML itself, this would cause poetry to manage itself and raise unexpected errors. ### Installing dependencies diff --git a/_static/demonstration_assets/adjoint_diff/scaling.png b/_static/demonstration_assets/adjoint_diff/scaling.png index 88cbb01b89..6b4019f1da 100644 Binary files a/_static/demonstration_assets/adjoint_diff/scaling.png and b/_static/demonstration_assets/adjoint_diff/scaling.png differ diff --git a/_static/demonstration_assets/quantum_volume/fake_lima.png b/_static/demonstration_assets/quantum_volume/fake_lima.png new file mode 100644 index 0000000000..b11add1579 Binary files /dev/null and b/_static/demonstration_assets/quantum_volume/fake_lima.png differ diff --git a/_static/demonstration_assets/quantum_volume/lima.svg b/_static/demonstration_assets/quantum_volume/lima.svg deleted file mode 100644 index 4a1a23aef5..0000000000 --- a/_static/demonstration_assets/quantum_volume/lima.svg +++ /dev/null @@ -1,233 +0,0 @@ - - - - - - - - - 2020-12-02T08:03:57.204934 - image/svg+xml - - - Matplotlib v3.3.1, https://matplotlib.org/ - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - diff --git a/conf.py b/conf.py index 10a9a472a5..610c654035 100644 --- a/conf.py +++ b/conf.py @@ -18,12 +18,17 @@ import numpy as np from jinja2 import FileSystemLoader, Environment import yaml -from pennylane import PennyLaneDeprecationWarning from pathlib import Path sys.path.insert(0, os.path.abspath(".")) sys.path.insert(0, os.path.join(os.path.dirname(__file__))) +# This module has been refactored from pennylane v0.41.0 to v0.42.0 +# This can be removed after the release of v0.42.0 +try: + from pennylane.exceptions import PennyLaneDeprecationWarning +except ModuleNotFoundError: + from pennylane import PennyLaneDeprecationWarning # -- Project information ----------------------------------------------------- # General information about the project. diff --git a/demonstrations/adjoint_diff_benchmarking.metadata.json b/demonstrations/adjoint_diff_benchmarking.metadata.json index 2c5c4f4bf3..f27bb77c34 100644 --- a/demonstrations/adjoint_diff_benchmarking.metadata.json +++ b/demonstrations/adjoint_diff_benchmarking.metadata.json @@ -6,7 +6,7 @@ } ], "dateOfPublication": "2021-11-23T00:00:00+00:00", - "dateOfLastModification": "2024-10-07T00:00:00+00:00", + "dateOfLastModification": "2025-07-11T00:00:00+00:00", "categories": [ "Getting Started" ], diff --git a/demonstrations/adjoint_diff_benchmarking.py b/demonstrations/adjoint_diff_benchmarking.py index c907290962..4ae968d47c 100644 --- a/demonstrations/adjoint_diff_benchmarking.py +++ b/demonstrations/adjoint_diff_benchmarking.py @@ -22,10 +22,7 @@ import timeit import matplotlib.pyplot as plt import pennylane as qml -import jax - -jax.config.update("jax_platform_name", "cpu") -jax.config.update('jax_enable_x64', True) +import pennylane.numpy as pnp plt.style.use("bmh") @@ -33,12 +30,12 @@ def get_time(qnode, params): - globals_dict = {'grad': jax.grad, 'circuit': qnode, 'params': params} + globals_dict = {"grad": qml.grad, "circuit": qnode, "params": params} return timeit.timeit("grad(circuit)(params)", globals=globals_dict, number=n_samples) def wires_scaling(n_wires, n_layers): - key = jax.random.PRNGKey(42) + rng = pnp.random.default_rng(12345) t_adjoint = [] t_ps = [] @@ -58,7 +55,7 @@ def circuit(params, wires): # set up the parameters param_shape = qml.StronglyEntanglingLayers.shape(n_wires=i_wires, n_layers=n_layers) - params = jax.random.normal(key, param_shape) + params = rng.normal(size=pnp.prod(param_shape), requires_grad=True).reshape(param_shape) t_adjoint.append(get_time(circuit_adjoint, params)) t_backprop.append(get_time(circuit_backprop, params)) @@ -68,10 +65,10 @@ def circuit(params, wires): def layers_scaling(n_wires, n_layers): - key = jax.random.PRNGKey(42) + rng = pnp.random.default_rng(12345) dev = qml.device("lightning.qubit", wires=n_wires) - dev_python = qml.device('default.qubit', wires=n_wires) + dev_python = qml.device("default.qubit", wires=n_wires) t_adjoint = [] t_ps = [] @@ -88,7 +85,7 @@ def circuit(params): for i_layers in n_layers: # set up the parameters param_shape = qml.StronglyEntanglingLayers.shape(n_wires=n_wires, n_layers=i_layers) - params = jax.random.normal(key, param_shape) + params = rng.normal(size=pnp.prod(param_shape), requires_grad=True).reshape(param_shape) t_adjoint.append(get_time(circuit_adjoint, params)) t_backprop.append(get_time(circuit_backprop, params)) @@ -110,9 +107,9 @@ def circuit(params): # Generating the graphic fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(10, 4)) - ax1.plot(wires_list, adjoint_wires, '.-', label="adjoint") - ax1.plot(wires_list, ps_wires, '.-', label="parameter-shift") - ax1.plot(wires_list, backprop_wires, '.-', label="backprop") + ax1.plot(wires_list, adjoint_wires, ".-", label="adjoint") + ax1.plot(wires_list, ps_wires, ".-", label="parameter-shift") + ax1.plot(wires_list, backprop_wires, ".-", label="backprop") ax1.legend() @@ -122,16 +119,17 @@ def circuit(params): ax1.set_yscale("log") ax1.set_title("Scaling with wires") - ax2.plot(layers_list, adjoint_layers, '.-', label="adjoint") - ax2.plot(layers_list, ps_layers, '.-', label="parameter-shift") - ax2.plot(layers_list, backprop_layers, '.-', label="backprop") + ax2.plot(layers_list, adjoint_layers, ".-", label="adjoint") + ax2.plot(layers_list, ps_layers, ".-", label="parameter-shift") + ax2.plot(layers_list, backprop_layers, ".-", label="backprop") ax2.legend() ax2.set_xlabel("Number of layers") ax2.set_xticks(layers_list) - ax2.set_ylabel("Time") - ax2.set_title("Scaling with Layers") + ax2.set_ylabel("Log Time") + ax2.set_yscale("log") + ax2.set_title("Scaling with layers") plt.savefig("scaling.png") diff --git a/demonstrations/ahs_aquila.metadata.json b/demonstrations/ahs_aquila.metadata.json index fc530f8d99..9c4d2538d2 100644 --- a/demonstrations/ahs_aquila.metadata.json +++ b/demonstrations/ahs_aquila.metadata.json @@ -6,7 +6,7 @@ } ], "dateOfPublication": "2023-05-16T00:00:00+00:00", - "dateOfLastModification": "2024-10-07T00:00:00+00:00", + "dateOfLastModification": "2025-07-11T00:00:00+00:00", "categories": ["Quantum Hardware", "Devices and Performance", "Quantum Computing"], "tags": [], "previewImages": [ diff --git a/demonstrations/ahs_aquila.py b/demonstrations/ahs_aquila.py index c579b2f385..722347ad61 100644 --- a/demonstrations/ahs_aquila.py +++ b/demonstrations/ahs_aquila.py @@ -132,7 +132,7 @@ The modification of the energy levels when atoms are in proximity gives rise to Rydberg blockade, where atoms that have been driven by a pulse that would, in isolation, leave them in the excited state -instead remain in the ground state due to neighboring atoms being excited. The distance within which two +instead remain in the ground state due to neighboring atoms being excited. The distance within which two neighboring atoms are effectively prevented from both being excited is referred to as the *blockade radius* :math:`R_b.` This brings us to our discussion of the second part of the Hamiltonian: the drive term. @@ -220,6 +220,16 @@ device_arn="arn:aws:braket:us-east-1::device/qpu/quera/Aquila", wires=3, ) +############################################################################## +# .. rst-class:: sphx-glr-script-out +# +# +# .. code-block:: none +# +# pennylane/pennylane/__init__.py:201: PennyLaneDeprecationWarning: pennylane.QuantumFunctionError is no longer accessible at top-level +# and must be imported as pennylane.exceptions.QuantumFunctionError. Support for top-level access will be removed in v0.43. +# warnings.warn( +# rydberg_simulator = qml.device("braket.local.ahs", wires=3) diff --git a/demonstrations/getting_started_with_hybrid_jobs.metadata.json b/demonstrations/getting_started_with_hybrid_jobs.metadata.json index b7b67ddfe9..b64b638561 100644 --- a/demonstrations/getting_started_with_hybrid_jobs.metadata.json +++ b/demonstrations/getting_started_with_hybrid_jobs.metadata.json @@ -6,7 +6,7 @@ } ], "dateOfPublication": "2023-10-16T00:00:00+00:00", - "dateOfLastModification": "2025-01-14T00:00:00+00:00", + "dateOfLastModification": "2025-07-11T00:00:00+00:00", "categories": [ "Devices and Performance" ], diff --git a/demonstrations/getting_started_with_hybrid_jobs.py b/demonstrations/getting_started_with_hybrid_jobs.py index 1d97626328..a5d5f159e9 100644 --- a/demonstrations/getting_started_with_hybrid_jobs.py +++ b/demonstrations/getting_started_with_hybrid_jobs.py @@ -10,7 +10,7 @@ In this tutorial, we'll walk through how to create your first hybrid quantum-classical algorithms on AWS. With a single-line-of-code, we'll see how to scale from PennyLane simulators on your laptop to running full-scale experiments on AWS that leverage both powerful classical compute and quantum devices. -You'll gain understanding of the hybrid jobs queue, including QPU priority queuing, and learn how to scale classical resources for resource-intensive tasks. +You'll gain understanding of the hybrid jobs queue, including QPU priority queuing, and learn how to scale classical resources for resource-intensive tasks. We hope these tools will empower you to start experimenting today with hybrid quantum algorithms! .. figure:: /_static/demonstration_assets/getting_started_with_hybrid_jobs/socialthumbnail_large_getting_started_with_hybrid_jobs.png @@ -40,8 +40,8 @@ time, enabling you to keep track of costs, budget, or custom metrics such as training loss or expectation values. -Importantly, on specific QPUs, running your algorithm in Hybrid Jobs benefits from `parametric compilation `__. -This reduces the overhead associated with the computationally expensive compilation step by compiling a circuit only once and not for every iteration in your hybrid algorithm. +Importantly, on specific QPUs, running your algorithm in Hybrid Jobs benefits from `parametric compilation `__. +This reduces the overhead associated with the computationally expensive compilation step by compiling a circuit only once and not for every iteration in your hybrid algorithm. This dramatically reduces the total runtime for many variational algorithms. For long-running hybrid jobs, Braket automatically uses the updated calibration data from the hardware provider when compiling your circuit to ensure the highest quality results. @@ -67,6 +67,15 @@ device = qml.device("braket.local.qubit", wires=1) +############################################################################## +# .. rst-class:: sphx-glr-script-out +# +# .. code-block:: none +# +# pennylane/__init__.py:200: PennyLaneDeprecationWarning: pennylane.QuantumFunctionError is no longer accessible at top-level +# and must be imported as pennylane.exceptions.QuantumFunctionError. Support for top-level access will be removed in v0.43. +# warnings.warn( + ###################################################################### # Now we define a circuit with two rotation gates and measure the expectation value in the # :math:`Z`-basis. @@ -246,6 +255,22 @@ def circuit(params): # Additionally, we can plot the metrics recorded during the algorithm. Below we show the expectation # value decreases with each iteration as expected. # +# .. note:: +# +# ``job.metrics()`` will be an empty dictionary while the job is running, and likely also for +# a few minutes afterwards. To make sure that the below plot generates correctly, it is recommended +# to block execution until the metrics are correctly reported. This can be done by: +# +# * using a ``while`` loop to manually block execution until the metrics are available. +# * using a Python debugger and setting a breakpoint before the line that uses ``job.metrics()`` +# and blocking as needed. +# +# We will use a ``while`` loop as shown below. +# + +while len(job.metrics()) == 0: + pass + import pandas as pd import matplotlib.pyplot as plt @@ -304,8 +329,8 @@ def circuit(params): # .. note:: # AWS devices must be declared within the body of the decorated function. # -# .. warning:: -# The Rigetti device used in this demo, AspenM3, has been retired. For an updated list of available hardware through +# .. warning:: +# The Rigetti device used in this demo, AspenM3, has been retired. For an updated list of available hardware through # Amazon Braket, please consult the `supported regions and devices documentation `__. The general steps outlined below still hold regardless of the choice of device, though. # diff --git a/demonstrations/how_to_catalyst_lightning_gpu.metadata.json b/demonstrations/how_to_catalyst_lightning_gpu.metadata.json index 7980d6161d..91a02843a2 100644 --- a/demonstrations/how_to_catalyst_lightning_gpu.metadata.json +++ b/demonstrations/how_to_catalyst_lightning_gpu.metadata.json @@ -9,7 +9,7 @@ } ], "dateOfPublication": "2025-02-21T10:00:00+00:00", - "dateOfLastModification": "2025-02-21T10:00:00+00:00", + "dateOfLastModification": "2025-07-11T10:00:00+00:00", "categories": [ "Getting Started", "Quantum Chemistry", diff --git a/demonstrations/how_to_catalyst_lightning_gpu.py b/demonstrations/how_to_catalyst_lightning_gpu.py index e4f1b4cf62..c161749bed 100644 --- a/demonstrations/how_to_catalyst_lightning_gpu.py +++ b/demonstrations/how_to_catalyst_lightning_gpu.py @@ -107,7 +107,7 @@ # # .. code-block:: none # -# 1.7712995142661776 +# 1.2454125296886156 # # Lightning-GPU has feature parity with `the rest of Lightning state-vector simulators `__, # providing native support for many PennyLane's operations and measurement processes with @@ -141,7 +141,7 @@ # # .. code-block:: none # -# [ 8.8817842e-16 ... -6.2915415e-01 0.0000000e+00] +# [ -1.1102230e-16 ... -7.2645772e-01 0.0000000e+00] # # # Note that in the above example, we didn't use ``method="adjoint"``. @@ -194,7 +194,7 @@ # Step = 7 # Step = 8 # Step = 9 -# [1.24479175e-01 ... 9.45755959e-01 4.64060426e-01] +# [0.947667 ... 3.093198 0.8401809 ] # # # We used `Optax `__, diff --git a/demonstrations/how_to_use_qiskit1_with_pennylane.metadata.json b/demonstrations/how_to_use_qiskit1_with_pennylane.metadata.json index c5a0361540..bdd35ebf63 100644 --- a/demonstrations/how_to_use_qiskit1_with_pennylane.metadata.json +++ b/demonstrations/how_to_use_qiskit1_with_pennylane.metadata.json @@ -6,7 +6,7 @@ } ], "dateOfPublication": "2024-07-02T00:00:00+00:00", - "dateOfLastModification": "2025-01-08T00:00:00+00:00", + "dateOfLastModification": "2025-07-10T00:00:00+00:00", "categories": [ "Quantum Computing", "How-to" diff --git a/demonstrations/how_to_use_qiskit1_with_pennylane.py b/demonstrations/how_to_use_qiskit1_with_pennylane.py index 8bc5ab1805..5907cf5e6a 100644 --- a/demonstrations/how_to_use_qiskit1_with_pennylane.py +++ b/demonstrations/how_to_use_qiskit1_with_pennylane.py @@ -113,7 +113,7 @@ def circuit(): # # .. code-block:: none # -# {'0': tensor(474, requires_grad=True), '1': tensor(550, requires_grad=True)} +# {'0': 474, '1': 550} # ###################################################################### @@ -403,7 +403,7 @@ def cost(phis, theta): # .. code-block:: none # # Optimized parameters: phis = [3.12829384 3.12823583], theta = [3.1310224] -# Optimized cost function val: -2.999796472821245 +# Optimized cost function value: -2.999796472821245 # ###################################################################### diff --git a/demonstrations/linear_equations_hhl_qrisp_catalyst.metadata.json b/demonstrations/linear_equations_hhl_qrisp_catalyst.metadata.json index 2e746833cc..e63e2a2c8d 100644 --- a/demonstrations/linear_equations_hhl_qrisp_catalyst.metadata.json +++ b/demonstrations/linear_equations_hhl_qrisp_catalyst.metadata.json @@ -12,7 +12,7 @@ } ], "dateOfPublication": "2025-02-26T09:00:00+00:00", - "dateOfLastModification": "2025-02-26T09:00:00+00:00", + "dateOfLastModification": "2025-07-15T09:00:00+00:00", "categories": [ "Algorithms", "Quantum Computing" diff --git a/demonstrations/linear_equations_hhl_qrisp_catalyst.py b/demonstrations/linear_equations_hhl_qrisp_catalyst.py index 61630f8759..82edbe7706 100644 --- a/demonstrations/linear_equations_hhl_qrisp_catalyst.py +++ b/demonstrations/linear_equations_hhl_qrisp_catalyst.py @@ -40,7 +40,8 @@ components easily. We'll demonstrate this later in this demo. You can install Qrisp to experiment with this implementation yourself, at your own pace, by calling -``pip install qrisp``. +``pip install qrisp``. Please note that `qrisp` is only compatible with PennyLane versions +<= 0.41.1 and Catalyst versions <= 0.11.0. QuantumVariable ~~~~~~~~~~~~~~~ diff --git a/demonstrations/ml_classical_shadows.metadata.json b/demonstrations/ml_classical_shadows.metadata.json index 5bcf2a0116..b8c4d33861 100644 --- a/demonstrations/ml_classical_shadows.metadata.json +++ b/demonstrations/ml_classical_shadows.metadata.json @@ -6,7 +6,7 @@ } ], "dateOfPublication": "2022-05-02T00:00:00+00:00", - "dateOfLastModification": "2025-01-10T00:00:00+00:00", + "dateOfLastModification": "2025-07-15T00:00:00+00:00", "categories": [ "Quantum Machine Learning" ], diff --git a/demonstrations/ml_classical_shadows.py b/demonstrations/ml_classical_shadows.py index 66bca15783..6569a61f48 100644 --- a/demonstrations/ml_classical_shadows.py +++ b/demonstrations/ml_classical_shadows.py @@ -27,8 +27,11 @@ properties from the learned classical shadows. So let's get started! .. note:: - This demo is compatible with the latest version of PennyLane and ``neural-tangents==0.6.5``. + This demo is compatible with the latest version of PennyLane and ``neural-tangents``. The latter is required for building the kernel for the infinite network used in training. + As of July 10th, 2025, the latest version of ``neural-tangents`` (v0.6.5) is only compatible + with ``jax<0.6.0``. This means that this demo is only compatible with PennyLane versions + <= 0.41.1 and Catalyst versions <= 0.11.0. Building the 2D Heisenberg model @@ -648,7 +651,7 @@ def build_dataset(num_points, Nr, Nc, T=500): # from the ``sklearn`` library. # -from sklearn.metrics import mean_squared_error +from sklearn.metrics import root_mean_squared_error def fit_predict_data(cij, kernel, opt="linear"): @@ -685,8 +688,8 @@ def fit_predict_data(cij, kernel, opt="linear"): best_model = model(hyperparam).fit(X_train, y_train) best_pred = best_model.predict(X_test) best_cv_score = cv_score - best_test_score = mean_squared_error( - best_model.predict(X_test).ravel(), y_test_clean.ravel(), squared=False + best_test_score = root_mean_squared_error( + best_model.predict(X_test).ravel(), y_test_clean.ravel() ) return ( diff --git a/demonstrations/qnn_module_tf.metadata.json b/demonstrations/qnn_module_tf.metadata.json index 15d489d26a..95a5882205 100644 --- a/demonstrations/qnn_module_tf.metadata.json +++ b/demonstrations/qnn_module_tf.metadata.json @@ -6,7 +6,7 @@ } ], "dateOfPublication": "2020-11-02T00:00:00+00:00", - "dateOfLastModification": "2025-03-21T00:00:00+00:00", + "dateOfLastModification": "2025-07-10T00:00:00+00:00", "categories": [ "Devices and Performance", "Quantum Machine Learning" diff --git a/demonstrations/qnn_module_tf.py b/demonstrations/qnn_module_tf.py index d6cc1e5a91..43a9e5c872 100644 --- a/demonstrations/qnn_module_tf.py +++ b/demonstrations/qnn_module_tf.py @@ -18,6 +18,8 @@ .. warning:: This demo is only compatible with PennyLane version 0.40 or below. + Tensorflow should also be version 2.15 for this demo to run. The package scikit-learn is + also required (do not install sklearn as it is deprecated). For usage with a later version of PennyLane, please consider using :doc:`PyTorch ` or :doc:`JAX `. diff --git a/demonstrations/qnspsa.metadata.json b/demonstrations/qnspsa.metadata.json index e93a848d74..fb2795e347 100644 --- a/demonstrations/qnspsa.metadata.json +++ b/demonstrations/qnspsa.metadata.json @@ -6,7 +6,7 @@ } ], "dateOfPublication": "2022-07-18T00:00:00+00:00", - "dateOfLastModification": "2025-06-05T00:00:00+00:00", + "dateOfLastModification": "2025-07-11T00:00:00+00:00", "categories": [ "Optimization" ], diff --git a/demonstrations/qnspsa.py b/demonstrations/qnspsa.py index 994846b6df..24df5fecf4 100644 --- a/demonstrations/qnspsa.py +++ b/demonstrations/qnspsa.py @@ -357,8 +357,8 @@ def get_grad(params_curr): # def get_overlap_tape(qnode, params1, params2): - tape_forward = qml.workflow.construct_tape(qnode)(*params1) - tape_inv = qml.workflow.construct_tape(qnode)(*params2) + tape_forward = qml.workflow.construct_tape(qnode)(params1) + tape_inv = qml.workflow.construct_tape(qnode)(params2) ops = tape_forward.operations + list(qml.adjoint(op) for op in reversed(tape_inv.operations)) return qml.tape.QuantumTape(ops, [qml.probs(wires=tape_forward.wires)]) @@ -819,8 +819,8 @@ def __get_spsa_grad_tapes(self, cost, params): # used to estimate the gradient per optimization step. The sampled # direction is of the shape of the input parameter. direction = self.__get_perturbation_direction(params) - tape_forward = qml.workflow.construct_tape(cost)(*[params + self.finite_diff_step * direction]) - tape_backward = qml.workflow.construct_tape(cost)(*[params - self.finite_diff_step * direction]) + tape_forward = qml.workflow.construct_tape(cost)(params + self.finite_diff_step * direction) + tape_backward = qml.workflow.construct_tape(cost)(params - self.finite_diff_step * direction) return [tape_forward, tape_backward], direction def __update_tensor(self, tensor_raw): @@ -863,8 +863,8 @@ def __regularize_tensor(self, metric_tensor): def __apply_blocking(self, cost, params_curr, params_next): # For numerical stability: apply the blocking condition on the parameter update. - tape_loss_curr = qml.workflow.construct_tape(cost)(*[params_curr]) - tape_loss_next = qml.workflow.construct_tape(cost)(*[params_next]) + tape_loss_curr = qml.workflow.construct_tape(cost)(params_curr) + tape_loss_next = qml.workflow.construct_tape(cost)(params_next) loss_curr, loss_next = qml.execute([tape_loss_curr, tape_loss_next], cost.device, None) # self.k has been updated earlier. @@ -913,9 +913,9 @@ def __apply_blocking(self, cost, params_curr, params_next): # The optimizer performs reasonably well: the loss drops over optimization # steps and converges finally. We then reproduce the benchmarking test # between the gradient descent, quantum natural gradient descent, SPSA and -# QN-SPSA in Fig. 1(b) of reference [#Gacon2021]_ with the following job. +# QN-SPSA in Fig. 1(b) of reference [#Gacon2021]_ with the following job. # -# .. warning:: +# .. warning:: # The code required to plot the results of the example below is not provided, but a similar # example can be found in this # `notebook `__, diff --git a/demonstrations/quantum_volume.metadata.json b/demonstrations/quantum_volume.metadata.json index 4a626d10ed..05e2817552 100644 --- a/demonstrations/quantum_volume.metadata.json +++ b/demonstrations/quantum_volume.metadata.json @@ -6,7 +6,7 @@ } ], "dateOfPublication": "2020-12-15T00:00:00+00:00", - "dateOfLastModification": "2025-01-08T00:00:00+00:00", + "dateOfLastModification": "2025-07-11T00:00:00+00:00", "categories": [ "Quantum Hardware", "Quantum Computing" diff --git a/demonstrations/quantum_volume.py b/demonstrations/quantum_volume.py index 08d3783992..057151eb96 100644 --- a/demonstrations/quantum_volume.py +++ b/demonstrations/quantum_volume.py @@ -15,7 +15,7 @@ *Author: Olivia Di Matteo — Posted: 15 December 2020. Last updated: 15 April 2021.* .. warning:: - The data in this demo was originally generated using system calibration data from the ``ibmq_lima`` device, + The data in this demo was originally generated using system calibration data from the ``ibmq_lima`` device, which has since been retired. While the code now uses a fake version of the Lima device, the noise and calibration may have shifted since the data was generated. @@ -161,6 +161,7 @@ # The median of this probability distribution is: import numpy as np + prob_array = np.fromiter(measurement_probs.values(), dtype=np.float64) print(f"Median = {np.median(prob_array):.3f}") @@ -387,11 +388,10 @@ def qv_circuit_layer(num_qubits): for _ in range(m): qv_circuit_layer(m) -(expanded_tape, ), _ = qml.transforms.decompose(tape, gate_set={qml.QubitUnitary, qml.SWAP}) +(expanded_tape,), _ = qml.transforms.decompose(tape, gate_set={qml.QubitUnitary, qml.SWAP}) print(qml.drawer.tape_text(expanded_tape, wire_order=dev_ideal.wires, show_all_wires=True, show_matrices=True)) - ############################################################################## # .. rst-class:: sphx-glr-script-out # @@ -458,6 +458,7 @@ def qv_circuit_layer(num_qubits): # distribution as follows: # + def heavy_output_set(m, probs): # Compute heavy outputs of an m-qubit circuit with measurement outcome # probabilities given by probs, which is an array with the probabilities @@ -490,7 +491,7 @@ def heavy_output_set(m, probs): # Run the circuit, compute heavy outputs, and print results [output_probs] = qml.execute([tape], dev_ideal) # returns a list of result ! -output_probs = output_probs.reshape(2 ** m, ) +output_probs = output_probs.reshape(2**m) heavy_outputs, prob_heavy_output = heavy_output_set(m, output_probs) print("State\tProbability") @@ -542,8 +543,8 @@ def heavy_output_set(m, probs): # # .. code-block:: python3 # -# from qiskit_ibm_provider import IBMProvider -# IBMProvider.save_account('MY_API_TOKEN') +# from qiskit_ibm_runtime import QiskitRuntimeService +# QiskitRuntimeService.save_account(channel="ibm_quantum_platform", token="", overwrite=True, set_as_default=True) # # A token can be generated by logging into your IBM Q account `here `_ . # @@ -561,13 +562,15 @@ def heavy_output_set(m, probs): from rustworkx.visualization import mpl_draw from qiskit_ibm_runtime.fake_provider import FakeLimaV2 +from matplotlib import pyplot as plt mpl_draw(FakeLimaV2().coupling_map.graph) +plt.show() ############################################################################## # -# .. figure:: ../_static/demonstration_assets/quantum_volume/lima.svg +# .. figure:: ../_static/demonstration_assets/quantum_volume/fake_lima.png # :align: center # :width: 75% # @@ -590,11 +593,11 @@ def heavy_output_set(m, probs): transpile_args = { - "optimization_level": 3, - "coupling_map": FakeLimaV2().coupling_map, - "layout_method": "sabre", - "routing_method": "sabre", - } + "optimization_level": 3, + "coupling_map": FakeLimaV2().coupling_map, + "layout_method": "sabre", + "routing_method": "sabre", +} dev_noisy = qml.device("qiskit.remote", wires=5, shots=1000, backend=FakeLimaV2(), **transpile_args) @@ -624,22 +627,19 @@ def heavy_output_set(m, probs): for _ in range(m): qv_circuit_layer(m) qml.probs(wires=range(m)) - + # when using qml.execute, shots must be on the tape tape_counts = tape_probs.copy(measurements=[qml.counts()], shots=1000) output_probs = qml.execute([tape_probs], dev_ideal) - output_probs = output_probs[0].reshape(2 ** m, ) + output_probs = output_probs[0].reshape(2**m) heavy_outputs, prob_heavy_output = heavy_output_set(m, output_probs) # Execute circuit on the noisy device [counts] = qml.execute([tape_counts], dev_noisy) device_heavy_outputs = np.sum( - [ - counts[x] if x[:m] in heavy_outputs else 0 - for x in counts.keys() - ] + [counts[x] if x[:m] in heavy_outputs else 0 for x in counts.keys()] ) fraction_device_heavy_output = device_heavy_outputs / dev_noisy.shots.total_shots @@ -726,7 +726,6 @@ def heavy_output_set(m, probs): # :math:`2\sigma` away from the threshold! # -from matplotlib import pyplot as plt fig, ax = plt.subplots(2, 2, sharex=True, sharey=True, figsize=(9, 6)) ax = ax.ravel() diff --git a/demonstrations/tutorial_How_to_simulate_quantum_circuits_with_tensor_networks.metadata.json b/demonstrations/tutorial_How_to_simulate_quantum_circuits_with_tensor_networks.metadata.json index 7065e33abc..5a56780fff 100644 --- a/demonstrations/tutorial_How_to_simulate_quantum_circuits_with_tensor_networks.metadata.json +++ b/demonstrations/tutorial_How_to_simulate_quantum_circuits_with_tensor_networks.metadata.json @@ -6,7 +6,7 @@ } ], "dateOfPublication": "2024-07-09T00:00:00+00:00", - "dateOfLastModification": "2024-10-07T00:00:00+00:00", + "dateOfLastModification": "2025-06-25T00:00:00+00:00", "categories": [ "Getting Started", "Quantum Computing", diff --git a/demonstrations/tutorial_QGAN.metadata.json b/demonstrations/tutorial_QGAN.metadata.json index 2cf2f49c60..a8a1788db2 100644 --- a/demonstrations/tutorial_QGAN.metadata.json +++ b/demonstrations/tutorial_QGAN.metadata.json @@ -6,7 +6,7 @@ } ], "dateOfPublication": "2019-10-11T00:00:00+00:00", - "dateOfLastModification": "2024-10-07T00:00:00+00:00", + "dateOfLastModification": "2025-06-10T00:00:00+00:00", "categories": [ "Quantum Machine Learning" ], diff --git a/demonstrations/tutorial_QGAN.py b/demonstrations/tutorial_QGAN.py index bc51ac55e2..ff9b6adeba 100644 --- a/demonstrations/tutorial_QGAN.py +++ b/demonstrations/tutorial_QGAN.py @@ -212,9 +212,12 @@ def gen_cost(gen_weights): cost = lambda: disc_cost(disc_weights) for step in range(50): - opt.minimize(cost, [disc_weights]) + with tf.GradientTape() as tape: + loss_value = cost() + gradients = tape.gradient(loss_value, [disc_weights]) + opt.apply_gradients(zip(gradients, [disc_weights])) if step % 5 == 0: - cost_val = cost().numpy() + cost_val = loss_value.numpy() print("Step {}: cost = {}".format(step, cost_val)) @@ -242,9 +245,12 @@ def gen_cost(gen_weights): cost = lambda: gen_cost(gen_weights) for step in range(50): - opt.minimize(cost, [gen_weights]) + with tf.GradientTape() as tape: + loss_value = cost() + gradients = tape.gradient(loss_value, [gen_weights]) + opt.apply_gradients(zip(gradients, [gen_weights])) if step % 5 == 0: - cost_val = cost().numpy() + cost_val = loss_value.numpy() print("Step {}: cost = {}".format(step, cost_val)) diff --git a/demonstrations/tutorial_adjoint_diff.metadata.json b/demonstrations/tutorial_adjoint_diff.metadata.json index 95e9a2c6fc..ab4a089127 100644 --- a/demonstrations/tutorial_adjoint_diff.metadata.json +++ b/demonstrations/tutorial_adjoint_diff.metadata.json @@ -6,7 +6,7 @@ } ], "dateOfPublication": "2021-11-23T00:00:00+00:00", - "dateOfLastModification": "2024-10-07T00:00:00+00:00", + "dateOfLastModification": "2025-07-11T00:00:00+00:00", "categories": [ "Getting Started" ], diff --git a/demonstrations/tutorial_adjoint_diff.py b/demonstrations/tutorial_adjoint_diff.py index 5941243c92..3f1506443b 100644 --- a/demonstrations/tutorial_adjoint_diff.py +++ b/demonstrations/tutorial_adjoint_diff.py @@ -71,24 +71,21 @@ # To start, we import PennyLane and Jax's numpy: import pennylane as qml -import jax -from jax import numpy as np - -jax.config.update("jax_platform_name", "cpu") +import pennylane.numpy as pnp ############################################################################## # We also need a circuit to simulate: # -dev = qml.device('default.qubit', wires=2) +dev = qml.device("default.qubit", wires=2) -x = np.array([0.1, 0.2, 0.3]) +x = pnp.array([0.1, 0.2, 0.3], requires_grad=True) @qml.qnode(dev, diff_method="adjoint") def circuit(a): qml.RX(a[0], wires=0) - qml.CNOT(wires=(0,1)) + qml.CNOT(wires=(0, 1)) qml.RY(a[1], wires=1) qml.RZ(a[2], wires=1) return qml.expval(qml.PauliX(wires=1)) @@ -107,20 +104,20 @@ def circuit(a): ops = [ qml.RX(x[0], wires=0), - qml.CNOT(wires=(0,1)), + qml.CNOT(wires=(0, 1)), qml.RY(x[1], wires=1), - qml.RZ(x[2], wires=1) + qml.RZ(x[2], wires=1), ] M = qml.PauliX(wires=1) ############################################################################## # We will be using internal functions to manipulate the nuts and bolts of a statevector # simulation. -# +# # Internally, the statevector simulation uses a 2x2x2x... array to represent the state, whereas # the result of a measurement ``qml.state()`` flattens this internal representation. Each dimension # in the statevector corresponds to a different qubit. -# +# # The internal functions ``create_initial_state`` and ``apply_operation`` # make additional assumptions about their inputs, and will fail or give incorrect results # if those assumptions are not met. To work with these simulation tools, all operations should provide @@ -161,7 +158,7 @@ def circuit(a): # Now we use ``np.vdot`` to take their inner product. ``np.vdot`` sums over all dimensions # and takes the complex conjugate of the first input. -M_expval = np.vdot(bra, ket) +M_expval = pnp.vdot(bra, ket) print("vdot : ", M_expval) print("QNode : ", circuit(x)) @@ -189,10 +186,10 @@ def circuit(a): ket_n = create_initial_state((0, 1)) -for op in ops[:-1]: # don't apply last operation +for op in ops[:-1]: # don't apply last operation ket_n = apply_operation(op, ket_n) -M_expval_n = np.vdot(bra_n, ket_n) +M_expval_n = pnp.vdot(bra_n, ket_n) print(M_expval_n) ############################################################################## @@ -227,7 +224,7 @@ def circuit(a): bra_n_v2 = apply_operation(adj_op, bra_n_v2) ket_n_v2 = apply_operation(adj_op, ket_n_v2) -M_expval_n_v2 = np.vdot(bra_n_v2, ket_n_v2) +M_expval_n_v2 = pnp.vdot(bra_n_v2, ket_n_v2) print(M_expval_n_v2) ############################################################################## @@ -256,7 +253,7 @@ def circuit(a): adj_op = qml.adjoint(op) bra_loop = apply_operation(adj_op, bra_loop) ket_loop = apply_operation(adj_op, ket_loop) - print(np.vdot(bra_loop, ket_loop)) + print(pnp.vdot(bra_loop, ket_loop)) ############################################################################## # Finally to Derivatives! @@ -370,7 +367,7 @@ def circuit(a): dU = qml.operation.operation_derivative(op) ket_temp = apply_operation(qml.QubitUnitary(dU, op.wires), ket) - dM = 2 * np.real(np.vdot(bra, ket_temp)) + dM = 2 * pnp.real(pnp.vdot(bra, ket_temp)) grads.append(dM) bra = apply_operation(adj_op, bra) @@ -382,7 +379,7 @@ def circuit(a): print("our calculation: ", [float(grad) for grad in grads]) -grad_compare = jax.grad(circuit)(x) +grad_compare = qml.grad(circuit)(x) print("comparison: ", grad_compare) ############################################################################## @@ -393,17 +390,17 @@ def circuit(a): # ``"default.qubit"`` or PennyLane's fast C++ simulator ``"lightning.qubit"``. -dev_lightning = qml.device('lightning.qubit', wires=2) +dev_lightning = qml.device("lightning.qubit", wires=2) @qml.qnode(dev_lightning, diff_method="adjoint") def circuit_adjoint(a): qml.RX(a[0], wires=0) - qml.CNOT(wires=(0,1)) + qml.CNOT(wires=(0, 1)) qml.RY(a[1], wires=1) qml.RZ(a[2], wires=1) return qml.expval(M) -print(jax.grad(circuit_adjoint)(x)) +print(qml.grad(circuit_adjoint)(x)) ############################################################################## # Performance diff --git a/demonstrations/tutorial_block_encoding.metadata.json b/demonstrations/tutorial_block_encoding.metadata.json index f9f3ca3656..1be9d62bfd 100644 --- a/demonstrations/tutorial_block_encoding.metadata.json +++ b/demonstrations/tutorial_block_encoding.metadata.json @@ -12,7 +12,7 @@ } ], "dateOfPublication": "2023-11-28T00:00:00+00:00", - "dateOfLastModification": "2025-05-27T00:00:00+00:00", + "dateOfLastModification": "2025-06-02T00:00:00+00:00", "categories": [ "Quantum Computing", "Algorithms" diff --git a/demonstrations/tutorial_block_encoding.py b/demonstrations/tutorial_block_encoding.py index 22494eaf9e..cc5f74d59b 100644 --- a/demonstrations/tutorial_block_encoding.py +++ b/demonstrations/tutorial_block_encoding.py @@ -104,8 +104,8 @@ # The next step is to identify and prepare the qubit registers used in the oracle access framework. # There are three registers :code:`"ancilla"`, :code:`"wires_i"`, :code:`"wires_j"`. The # :code:`"ancilla"` register will always contain a single qubit, this is the auxilary qubit where we -# apply the rotation gates mentioned above. The :code:`"wires_i"` and :code:`"wires_j"` registers are -# the same size for this algorithm and need to be able to encode :math:`A` itself, so they will both +# apply the rotation gates mentioned above. The :code:`"wires_i"` and :code:`"wires_j"` registers are +# the same size for this algorithm and need to be able to encode :math:`A` itself, so they will both # have :math:`2` qubits for our matrix. ancilla_wires = ["ancilla"] @@ -118,11 +118,8 @@ # Finally, we obtain the control wires for the C-NOT gates and a wire map that we later use to # translate the control wires into the wire registers we prepared. -code = gray_code(2 * np.log2(len(A))) -n_selections = len(code) - -control_wires = [int(np.log2(int(code[i], 2) ^ int(code[(i + 1) % - n_selections], 2))) for i in range(n_selections)] +code = gray_code(int(2 * np.log2(len(A)))) +control_wires = np.log2(code ^ np.roll(code, -1)).astype(int) wire_map = {control_index : wire for control_index, wire in enumerate(wires_j + wires_i)} @@ -236,16 +233,16 @@ def UA(thetas, control_wires, ancilla): # The quantum circuit for the oracle :math:`U_A,` presented above, accesses every entry of # :math:`A` and thus requires :math:`~ O(N^2)` gates to implement the oracle [#fable]_. In the # special cases where :math:`A` is structured and sparse, we can generate a more efficient quantum -# circuit representation for :math:`U_A` and :math:`U_B` [#sparse]_ by only keeping track of the -# non-zero entries of the matrix. +# circuit representation for :math:`U_A` and :math:`U_B` [#sparse]_ by only keeping track of the +# non-zero entries of the matrix. # # Let :math:`b(i,j)` be a function such that it takes a column index :math:`j` and returns the -# row index for the :math:`i^{th}` non-zero entry in that column of :math:`A.` Note, in this -# formulation, the :math:`|i\rangle` qubit register now refers to the number of non-zero entries +# row index for the :math:`i^{th}` non-zero entry in that column of :math:`A.` Note, in this +# formulation, the :math:`|i\rangle` qubit register now refers to the number of non-zero entries # in :math:`A.` For sparse matrices, this can be much smaller than :math:`N,` thus saving us many # qubits. We use this to define :math:`U_A` and :math:`U_B.` # -# Like in the structured approach, the :math:`U_A` oracle is responsible for encoding the matrix +# Like in the structured approach, the :math:`U_A` oracle is responsible for encoding the matrix # entries of :math:`A` into the amplitude of the ancilla qubit. However, we now use :math:`b(i,j)` # to access the row index of the non-zero entries: # @@ -253,7 +250,7 @@ def UA(thetas, control_wires, ancilla): # # U_A |0\rangle_{\text{anc}} |i\rangle |j\rangle = |A_{b(i,j),j}\rangle_{\text{anc}} |i\rangle |j\rangle, # -# In this case the :math:`U_B` oracle is responsible for implementing the :math:`b(i,j)` function +# In this case the :math:`U_B` oracle is responsible for implementing the :math:`b(i,j)` function # and taking us from the column index to the row index in the qubit register: # # .. math:: U_B |i\rangle|j\rangle \ = \ |i\rangle |b(i,j)\rangle @@ -286,7 +283,7 @@ def UA(thetas, control_wires, ancilla): ############################################################################## # Once again we identify and prepare the qubit registers used in the oracle access framework. -# +# # The :code:`"ancilla"` register will still contain a single qubit, the target for the # controlled rotation gates. The :code:`"wires_i"` register needs to be large enough to binary # encode the maximum number of non-zero entries in any column or row. Given the structure of diff --git a/demonstrations/tutorial_iqp_circuit_optimization_jax.metadata.json b/demonstrations/tutorial_iqp_circuit_optimization_jax.metadata.json index f045cffd4e..c65fc9b253 100644 --- a/demonstrations/tutorial_iqp_circuit_optimization_jax.metadata.json +++ b/demonstrations/tutorial_iqp_circuit_optimization_jax.metadata.json @@ -9,7 +9,7 @@ } ], "dateOfPublication": "2025-02-14T09:00:00+00:00", - "dateOfLastModification": "2025-02-14T09:00:00+00:00", + "dateOfLastModification": "2025-06-02T09:00:00+00:00", "categories": [ "Optimization", "Algorithms" diff --git a/demonstrations/tutorial_iqp_circuit_optimization_jax.py b/demonstrations/tutorial_iqp_circuit_optimization_jax.py index 211b1f3c22..821875da0d 100644 --- a/demonstrations/tutorial_iqp_circuit_optimization_jax.py +++ b/demonstrations/tutorial_iqp_circuit_optimization_jax.py @@ -134,11 +134,12 @@ def penn_iqp_gates(params: np.ndarray, gates: list, n_qubits: int): for i in range(n_qubits): qml.Hadamard(i) + ###################################################################### # Now we have our circuit, we can evaluate expectation values of tensor products of Pauli Z operators # specified by lists of the form ``op`` above. # -def penn_obs(op: np.ndarray) -> qml.operation.Observable: +def penn_obs(op: np.ndarray) -> qml.operation.Operator: """Returns a PennyLane observable from a bitstring representation. Args: @@ -158,6 +159,7 @@ def penn_obs(op: np.ndarray) -> qml.operation.Observable: obs @= qml.Z(i) return obs + def penn_iqp_circuit(params: np.ndarray, gates: list, op: np.ndarray, n_qubits: int) -> qml.measurements.ExpectationMP: """Defines the circuit that calculates the expectation value of the operator with the IQP circuit with PennyLane tools. @@ -594,22 +596,22 @@ def loss_fn(params, circuit, ops, n_samples, key): # # .. [#nest] # -# M. Van den Nest. +# M. Van den Nest. # "Simulating quantum computers with probabilistic methods" # `arXiv:0911.1624 `__, 2010. # # .. [#gretton] # -# Arthur Gretton, Karsten M. Borgwardt, Malte J. Rasch, Bernhard Schölkopf, Alexander Smola. +# Arthur Gretton, Karsten M. Borgwardt, Malte J. Rasch, Bernhard Schölkopf, Alexander Smola. # "A Kernel Two-Sample Test" # `http://jmlr.org/papers/v13/gretton12a.html `__, in Journal of Machine Learning Research 13.25, pp. 723-773, 2012. # # .. [#ravuri] # -# Suman Ravuri, Mélanie Rey, Shakir Mohamed, Marc Peter Deisenroth. +# Suman Ravuri, Mélanie Rey, Shakir Mohamed, Marc Peter Deisenroth. # "Understanding Deep Generative Models with Generalized Empirical Likelihoods" # `arXiv:2306.09780 `__, 2023. -# +# # About the authors # ---------------- -# \ No newline at end of file +# diff --git a/demonstrations/tutorial_odegen.metadata.json b/demonstrations/tutorial_odegen.metadata.json index df0ad04903..a84d59773b 100644 --- a/demonstrations/tutorial_odegen.metadata.json +++ b/demonstrations/tutorial_odegen.metadata.json @@ -6,7 +6,7 @@ } ], "dateOfPublication": "2023-12-12T00:00:00+00:00", - "dateOfLastModification": "2025-01-28T00:00:00+00:00", + "dateOfLastModification": "2025-07-08T00:00:00+00:00", "categories": [ "Optimization", "Quantum Computing", diff --git a/demonstrations/tutorial_odegen.py b/demonstrations/tutorial_odegen.py index 8894ff68d8..11577b6e82 100644 --- a/demonstrations/tutorial_odegen.py +++ b/demonstrations/tutorial_odegen.py @@ -296,7 +296,7 @@ def partial_step(grad_circuit, opt_state, theta): return thetas, energy -key = jax.random.PRNGKey(0) +key = jax.random.PRNGKey(888) theta0 = jax.random.normal(key, shape=(n_param_batch, tbins * 2)) thetaf_odegen, energy_odegen = run_opt(value_and_grad_jax, theta0) @@ -313,8 +313,9 @@ def partial_step(grad_circuit, opt_state, theta): ############################################################################## # We see that with analytic gradients (ODEgen), we can reach the ground state energy within 100 epochs, whereas with SPS gradients we cannot find the path -# towards the minimum due to the stochasticity of the gradient estimates. Note that both optimizations start from the same (random) initial point. -# This picture solidifies when repeating this procedure for multiple runs from different random initializations, as was demonstrated in [#Kottmann]_. +# towards the minimum due to the stochasticity of the gradient estimates. Note that the convergence of the optimization is sensitive to the initial guess. +# In this demonstration, both optimizations start from the same (random) initial point. This picture solidifies when repeating this procedure for multiple +# runs from different random initializations, as was demonstrated in [#Kottmann]_. # # We also want to make sure that this is a fair comparison in terms of quantum resources. In the case of ODEgen, we maximally have :math:`\mathcal{R}_\text{ODEgen} = 2 (4^n - 1) = 30` expectation values. # For SPS we have :math:`2 N_g N_s = 32` (due to :math:`N_g = 2` and :math:`N_s=8` time samples per gradient that we chose in ``num_split_times`` above). Thus, overall, we require fewer diff --git a/demonstrations/tutorial_pasqal.metadata.json b/demonstrations/tutorial_pasqal.metadata.json index f5decd0ac3..bcafc117f7 100644 --- a/demonstrations/tutorial_pasqal.metadata.json +++ b/demonstrations/tutorial_pasqal.metadata.json @@ -6,7 +6,7 @@ } ], "dateOfPublication": "2020-10-13T00:00:00+00:00", - "dateOfLastModification": "2024-10-07T00:00:00+00:00", + "dateOfLastModification": "2025-06-10T00:00:00+00:00", "categories": [ "Quantum Hardware", "Quantum Computing" diff --git a/demonstrations/tutorial_pasqal.py b/demonstrations/tutorial_pasqal.py index b49898f392..5bc8ae70e5 100644 --- a/demonstrations/tutorial_pasqal.py +++ b/demonstrations/tutorial_pasqal.py @@ -320,10 +320,13 @@ def cost(): output = (-circuit(weights, data) + 1) / 2 return tf.abs(output - label) ** 2 -opt = tf.keras.optimizers.Adam(learning_rate=0.1) +opt = tf.optimizers.Adam(learning_rate=0.1) for step in range(100): - opt.minimize(cost, [weights]) + with tf.GradientTape() as tape: + loss_value = cost() + gradients = tape.gradient(loss_value, [weights]) + opt.apply_gradients(zip(gradients, [weights])) if step % 5 == 0: print("Step {}: cost={}".format(step, cost())) diff --git a/demonstrations/tutorial_zne_catalyst.metadata.json b/demonstrations/zne_catalyst.metadata.json similarity index 100% rename from demonstrations/tutorial_zne_catalyst.metadata.json rename to demonstrations/zne_catalyst.metadata.json diff --git a/demonstrations/tutorial_zne_catalyst.py b/demonstrations/zne_catalyst.py similarity index 100% rename from demonstrations/tutorial_zne_catalyst.py rename to demonstrations/zne_catalyst.py diff --git a/demonstrations_v2/adjoint_diff_benchmarking/demo.py b/demonstrations_v2/adjoint_diff_benchmarking/demo.py index c907290962..4ae968d47c 100644 --- a/demonstrations_v2/adjoint_diff_benchmarking/demo.py +++ b/demonstrations_v2/adjoint_diff_benchmarking/demo.py @@ -22,10 +22,7 @@ import timeit import matplotlib.pyplot as plt import pennylane as qml -import jax - -jax.config.update("jax_platform_name", "cpu") -jax.config.update('jax_enable_x64', True) +import pennylane.numpy as pnp plt.style.use("bmh") @@ -33,12 +30,12 @@ def get_time(qnode, params): - globals_dict = {'grad': jax.grad, 'circuit': qnode, 'params': params} + globals_dict = {"grad": qml.grad, "circuit": qnode, "params": params} return timeit.timeit("grad(circuit)(params)", globals=globals_dict, number=n_samples) def wires_scaling(n_wires, n_layers): - key = jax.random.PRNGKey(42) + rng = pnp.random.default_rng(12345) t_adjoint = [] t_ps = [] @@ -58,7 +55,7 @@ def circuit(params, wires): # set up the parameters param_shape = qml.StronglyEntanglingLayers.shape(n_wires=i_wires, n_layers=n_layers) - params = jax.random.normal(key, param_shape) + params = rng.normal(size=pnp.prod(param_shape), requires_grad=True).reshape(param_shape) t_adjoint.append(get_time(circuit_adjoint, params)) t_backprop.append(get_time(circuit_backprop, params)) @@ -68,10 +65,10 @@ def circuit(params, wires): def layers_scaling(n_wires, n_layers): - key = jax.random.PRNGKey(42) + rng = pnp.random.default_rng(12345) dev = qml.device("lightning.qubit", wires=n_wires) - dev_python = qml.device('default.qubit', wires=n_wires) + dev_python = qml.device("default.qubit", wires=n_wires) t_adjoint = [] t_ps = [] @@ -88,7 +85,7 @@ def circuit(params): for i_layers in n_layers: # set up the parameters param_shape = qml.StronglyEntanglingLayers.shape(n_wires=n_wires, n_layers=i_layers) - params = jax.random.normal(key, param_shape) + params = rng.normal(size=pnp.prod(param_shape), requires_grad=True).reshape(param_shape) t_adjoint.append(get_time(circuit_adjoint, params)) t_backprop.append(get_time(circuit_backprop, params)) @@ -110,9 +107,9 @@ def circuit(params): # Generating the graphic fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(10, 4)) - ax1.plot(wires_list, adjoint_wires, '.-', label="adjoint") - ax1.plot(wires_list, ps_wires, '.-', label="parameter-shift") - ax1.plot(wires_list, backprop_wires, '.-', label="backprop") + ax1.plot(wires_list, adjoint_wires, ".-", label="adjoint") + ax1.plot(wires_list, ps_wires, ".-", label="parameter-shift") + ax1.plot(wires_list, backprop_wires, ".-", label="backprop") ax1.legend() @@ -122,16 +119,17 @@ def circuit(params): ax1.set_yscale("log") ax1.set_title("Scaling with wires") - ax2.plot(layers_list, adjoint_layers, '.-', label="adjoint") - ax2.plot(layers_list, ps_layers, '.-', label="parameter-shift") - ax2.plot(layers_list, backprop_layers, '.-', label="backprop") + ax2.plot(layers_list, adjoint_layers, ".-", label="adjoint") + ax2.plot(layers_list, ps_layers, ".-", label="parameter-shift") + ax2.plot(layers_list, backprop_layers, ".-", label="backprop") ax2.legend() ax2.set_xlabel("Number of layers") ax2.set_xticks(layers_list) - ax2.set_ylabel("Time") - ax2.set_title("Scaling with Layers") + ax2.set_ylabel("Log Time") + ax2.set_yscale("log") + ax2.set_title("Scaling with layers") plt.savefig("scaling.png") diff --git a/demonstrations_v2/adjoint_diff_benchmarking/metadata.json b/demonstrations_v2/adjoint_diff_benchmarking/metadata.json index 2c5c4f4bf3..f27bb77c34 100644 --- a/demonstrations_v2/adjoint_diff_benchmarking/metadata.json +++ b/demonstrations_v2/adjoint_diff_benchmarking/metadata.json @@ -6,7 +6,7 @@ } ], "dateOfPublication": "2021-11-23T00:00:00+00:00", - "dateOfLastModification": "2024-10-07T00:00:00+00:00", + "dateOfLastModification": "2025-07-11T00:00:00+00:00", "categories": [ "Getting Started" ], diff --git a/demonstrations_v2/ahs_aquila/demo.py b/demonstrations_v2/ahs_aquila/demo.py index c579b2f385..722347ad61 100644 --- a/demonstrations_v2/ahs_aquila/demo.py +++ b/demonstrations_v2/ahs_aquila/demo.py @@ -132,7 +132,7 @@ The modification of the energy levels when atoms are in proximity gives rise to Rydberg blockade, where atoms that have been driven by a pulse that would, in isolation, leave them in the excited state -instead remain in the ground state due to neighboring atoms being excited. The distance within which two +instead remain in the ground state due to neighboring atoms being excited. The distance within which two neighboring atoms are effectively prevented from both being excited is referred to as the *blockade radius* :math:`R_b.` This brings us to our discussion of the second part of the Hamiltonian: the drive term. @@ -220,6 +220,16 @@ device_arn="arn:aws:braket:us-east-1::device/qpu/quera/Aquila", wires=3, ) +############################################################################## +# .. rst-class:: sphx-glr-script-out +# +# +# .. code-block:: none +# +# pennylane/pennylane/__init__.py:201: PennyLaneDeprecationWarning: pennylane.QuantumFunctionError is no longer accessible at top-level +# and must be imported as pennylane.exceptions.QuantumFunctionError. Support for top-level access will be removed in v0.43. +# warnings.warn( +# rydberg_simulator = qml.device("braket.local.ahs", wires=3) diff --git a/demonstrations_v2/ahs_aquila/metadata.json b/demonstrations_v2/ahs_aquila/metadata.json index fc530f8d99..9c4d2538d2 100644 --- a/demonstrations_v2/ahs_aquila/metadata.json +++ b/demonstrations_v2/ahs_aquila/metadata.json @@ -6,7 +6,7 @@ } ], "dateOfPublication": "2023-05-16T00:00:00+00:00", - "dateOfLastModification": "2024-10-07T00:00:00+00:00", + "dateOfLastModification": "2025-07-11T00:00:00+00:00", "categories": ["Quantum Hardware", "Devices and Performance", "Quantum Computing"], "tags": [], "previewImages": [ diff --git a/demonstrations_v2/getting_started_with_hybrid_jobs/demo.py b/demonstrations_v2/getting_started_with_hybrid_jobs/demo.py index 1d97626328..a5d5f159e9 100644 --- a/demonstrations_v2/getting_started_with_hybrid_jobs/demo.py +++ b/demonstrations_v2/getting_started_with_hybrid_jobs/demo.py @@ -10,7 +10,7 @@ In this tutorial, we'll walk through how to create your first hybrid quantum-classical algorithms on AWS. With a single-line-of-code, we'll see how to scale from PennyLane simulators on your laptop to running full-scale experiments on AWS that leverage both powerful classical compute and quantum devices. -You'll gain understanding of the hybrid jobs queue, including QPU priority queuing, and learn how to scale classical resources for resource-intensive tasks. +You'll gain understanding of the hybrid jobs queue, including QPU priority queuing, and learn how to scale classical resources for resource-intensive tasks. We hope these tools will empower you to start experimenting today with hybrid quantum algorithms! .. figure:: /_static/demonstration_assets/getting_started_with_hybrid_jobs/socialthumbnail_large_getting_started_with_hybrid_jobs.png @@ -40,8 +40,8 @@ time, enabling you to keep track of costs, budget, or custom metrics such as training loss or expectation values. -Importantly, on specific QPUs, running your algorithm in Hybrid Jobs benefits from `parametric compilation `__. -This reduces the overhead associated with the computationally expensive compilation step by compiling a circuit only once and not for every iteration in your hybrid algorithm. +Importantly, on specific QPUs, running your algorithm in Hybrid Jobs benefits from `parametric compilation `__. +This reduces the overhead associated with the computationally expensive compilation step by compiling a circuit only once and not for every iteration in your hybrid algorithm. This dramatically reduces the total runtime for many variational algorithms. For long-running hybrid jobs, Braket automatically uses the updated calibration data from the hardware provider when compiling your circuit to ensure the highest quality results. @@ -67,6 +67,15 @@ device = qml.device("braket.local.qubit", wires=1) +############################################################################## +# .. rst-class:: sphx-glr-script-out +# +# .. code-block:: none +# +# pennylane/__init__.py:200: PennyLaneDeprecationWarning: pennylane.QuantumFunctionError is no longer accessible at top-level +# and must be imported as pennylane.exceptions.QuantumFunctionError. Support for top-level access will be removed in v0.43. +# warnings.warn( + ###################################################################### # Now we define a circuit with two rotation gates and measure the expectation value in the # :math:`Z`-basis. @@ -246,6 +255,22 @@ def circuit(params): # Additionally, we can plot the metrics recorded during the algorithm. Below we show the expectation # value decreases with each iteration as expected. # +# .. note:: +# +# ``job.metrics()`` will be an empty dictionary while the job is running, and likely also for +# a few minutes afterwards. To make sure that the below plot generates correctly, it is recommended +# to block execution until the metrics are correctly reported. This can be done by: +# +# * using a ``while`` loop to manually block execution until the metrics are available. +# * using a Python debugger and setting a breakpoint before the line that uses ``job.metrics()`` +# and blocking as needed. +# +# We will use a ``while`` loop as shown below. +# + +while len(job.metrics()) == 0: + pass + import pandas as pd import matplotlib.pyplot as plt @@ -304,8 +329,8 @@ def circuit(params): # .. note:: # AWS devices must be declared within the body of the decorated function. # -# .. warning:: -# The Rigetti device used in this demo, AspenM3, has been retired. For an updated list of available hardware through +# .. warning:: +# The Rigetti device used in this demo, AspenM3, has been retired. For an updated list of available hardware through # Amazon Braket, please consult the `supported regions and devices documentation `__. The general steps outlined below still hold regardless of the choice of device, though. # diff --git a/demonstrations_v2/getting_started_with_hybrid_jobs/metadata.json b/demonstrations_v2/getting_started_with_hybrid_jobs/metadata.json index b7b67ddfe9..b64b638561 100644 --- a/demonstrations_v2/getting_started_with_hybrid_jobs/metadata.json +++ b/demonstrations_v2/getting_started_with_hybrid_jobs/metadata.json @@ -6,7 +6,7 @@ } ], "dateOfPublication": "2023-10-16T00:00:00+00:00", - "dateOfLastModification": "2025-01-14T00:00:00+00:00", + "dateOfLastModification": "2025-07-11T00:00:00+00:00", "categories": [ "Devices and Performance" ], diff --git a/demonstrations_v2/how_to_catalyst_lightning_gpu/demo.py b/demonstrations_v2/how_to_catalyst_lightning_gpu/demo.py index e4f1b4cf62..c161749bed 100644 --- a/demonstrations_v2/how_to_catalyst_lightning_gpu/demo.py +++ b/demonstrations_v2/how_to_catalyst_lightning_gpu/demo.py @@ -107,7 +107,7 @@ # # .. code-block:: none # -# 1.7712995142661776 +# 1.2454125296886156 # # Lightning-GPU has feature parity with `the rest of Lightning state-vector simulators `__, # providing native support for many PennyLane's operations and measurement processes with @@ -141,7 +141,7 @@ # # .. code-block:: none # -# [ 8.8817842e-16 ... -6.2915415e-01 0.0000000e+00] +# [ -1.1102230e-16 ... -7.2645772e-01 0.0000000e+00] # # # Note that in the above example, we didn't use ``method="adjoint"``. @@ -194,7 +194,7 @@ # Step = 7 # Step = 8 # Step = 9 -# [1.24479175e-01 ... 9.45755959e-01 4.64060426e-01] +# [0.947667 ... 3.093198 0.8401809 ] # # # We used `Optax `__, diff --git a/demonstrations_v2/how_to_catalyst_lightning_gpu/metadata.json b/demonstrations_v2/how_to_catalyst_lightning_gpu/metadata.json index 7980d6161d..91a02843a2 100644 --- a/demonstrations_v2/how_to_catalyst_lightning_gpu/metadata.json +++ b/demonstrations_v2/how_to_catalyst_lightning_gpu/metadata.json @@ -9,7 +9,7 @@ } ], "dateOfPublication": "2025-02-21T10:00:00+00:00", - "dateOfLastModification": "2025-02-21T10:00:00+00:00", + "dateOfLastModification": "2025-07-11T10:00:00+00:00", "categories": [ "Getting Started", "Quantum Chemistry", diff --git a/demonstrations_v2/how_to_use_qiskit1_with_pennylane/demo.py b/demonstrations_v2/how_to_use_qiskit1_with_pennylane/demo.py index 8bc5ab1805..5907cf5e6a 100644 --- a/demonstrations_v2/how_to_use_qiskit1_with_pennylane/demo.py +++ b/demonstrations_v2/how_to_use_qiskit1_with_pennylane/demo.py @@ -113,7 +113,7 @@ def circuit(): # # .. code-block:: none # -# {'0': tensor(474, requires_grad=True), '1': tensor(550, requires_grad=True)} +# {'0': 474, '1': 550} # ###################################################################### @@ -403,7 +403,7 @@ def cost(phis, theta): # .. code-block:: none # # Optimized parameters: phis = [3.12829384 3.12823583], theta = [3.1310224] -# Optimized cost function val: -2.999796472821245 +# Optimized cost function value: -2.999796472821245 # ###################################################################### diff --git a/demonstrations_v2/how_to_use_qiskit1_with_pennylane/metadata.json b/demonstrations_v2/how_to_use_qiskit1_with_pennylane/metadata.json index c5a0361540..bdd35ebf63 100644 --- a/demonstrations_v2/how_to_use_qiskit1_with_pennylane/metadata.json +++ b/demonstrations_v2/how_to_use_qiskit1_with_pennylane/metadata.json @@ -6,7 +6,7 @@ } ], "dateOfPublication": "2024-07-02T00:00:00+00:00", - "dateOfLastModification": "2025-01-08T00:00:00+00:00", + "dateOfLastModification": "2025-07-10T00:00:00+00:00", "categories": [ "Quantum Computing", "How-to" diff --git a/demonstrations_v2/linear_equations_hhl_qrisp_catalyst/demo.py b/demonstrations_v2/linear_equations_hhl_qrisp_catalyst/demo.py index 61630f8759..82edbe7706 100644 --- a/demonstrations_v2/linear_equations_hhl_qrisp_catalyst/demo.py +++ b/demonstrations_v2/linear_equations_hhl_qrisp_catalyst/demo.py @@ -40,7 +40,8 @@ components easily. We'll demonstrate this later in this demo. You can install Qrisp to experiment with this implementation yourself, at your own pace, by calling -``pip install qrisp``. +``pip install qrisp``. Please note that `qrisp` is only compatible with PennyLane versions +<= 0.41.1 and Catalyst versions <= 0.11.0. QuantumVariable ~~~~~~~~~~~~~~~ diff --git a/demonstrations_v2/linear_equations_hhl_qrisp_catalyst/metadata.json b/demonstrations_v2/linear_equations_hhl_qrisp_catalyst/metadata.json index 2e746833cc..e63e2a2c8d 100644 --- a/demonstrations_v2/linear_equations_hhl_qrisp_catalyst/metadata.json +++ b/demonstrations_v2/linear_equations_hhl_qrisp_catalyst/metadata.json @@ -12,7 +12,7 @@ } ], "dateOfPublication": "2025-02-26T09:00:00+00:00", - "dateOfLastModification": "2025-02-26T09:00:00+00:00", + "dateOfLastModification": "2025-07-15T09:00:00+00:00", "categories": [ "Algorithms", "Quantum Computing" diff --git a/demonstrations_v2/ml_classical_shadows/demo.py b/demonstrations_v2/ml_classical_shadows/demo.py index 66bca15783..6569a61f48 100644 --- a/demonstrations_v2/ml_classical_shadows/demo.py +++ b/demonstrations_v2/ml_classical_shadows/demo.py @@ -27,8 +27,11 @@ properties from the learned classical shadows. So let's get started! .. note:: - This demo is compatible with the latest version of PennyLane and ``neural-tangents==0.6.5``. + This demo is compatible with the latest version of PennyLane and ``neural-tangents``. The latter is required for building the kernel for the infinite network used in training. + As of July 10th, 2025, the latest version of ``neural-tangents`` (v0.6.5) is only compatible + with ``jax<0.6.0``. This means that this demo is only compatible with PennyLane versions + <= 0.41.1 and Catalyst versions <= 0.11.0. Building the 2D Heisenberg model @@ -648,7 +651,7 @@ def build_dataset(num_points, Nr, Nc, T=500): # from the ``sklearn`` library. # -from sklearn.metrics import mean_squared_error +from sklearn.metrics import root_mean_squared_error def fit_predict_data(cij, kernel, opt="linear"): @@ -685,8 +688,8 @@ def fit_predict_data(cij, kernel, opt="linear"): best_model = model(hyperparam).fit(X_train, y_train) best_pred = best_model.predict(X_test) best_cv_score = cv_score - best_test_score = mean_squared_error( - best_model.predict(X_test).ravel(), y_test_clean.ravel(), squared=False + best_test_score = root_mean_squared_error( + best_model.predict(X_test).ravel(), y_test_clean.ravel() ) return ( diff --git a/demonstrations_v2/ml_classical_shadows/metadata.json b/demonstrations_v2/ml_classical_shadows/metadata.json index 5bcf2a0116..b8c4d33861 100644 --- a/demonstrations_v2/ml_classical_shadows/metadata.json +++ b/demonstrations_v2/ml_classical_shadows/metadata.json @@ -6,7 +6,7 @@ } ], "dateOfPublication": "2022-05-02T00:00:00+00:00", - "dateOfLastModification": "2025-01-10T00:00:00+00:00", + "dateOfLastModification": "2025-07-15T00:00:00+00:00", "categories": [ "Quantum Machine Learning" ], diff --git a/demonstrations_v2/qnn_module_tf/demo.py b/demonstrations_v2/qnn_module_tf/demo.py index cf4237416c..43a9e5c872 100644 --- a/demonstrations_v2/qnn_module_tf/demo.py +++ b/demonstrations_v2/qnn_module_tf/demo.py @@ -18,8 +18,10 @@ .. warning:: This demo is only compatible with PennyLane version 0.40 or below. + Tensorflow should also be version 2.15 for this demo to run. The package scikit-learn is + also required (do not install sklearn as it is deprecated). For usage with a later version of PennyLane, please consider using - :doc:`PyTorch ` or :doc:`JAX `. + :doc:`PyTorch ` or :doc:`JAX `. Creating neural networks in `Keras `__ is easy. Models are constructed from elementary *layers* and can be trained using a high-level API. For example, the following code diff --git a/demonstrations_v2/qnn_module_tf/metadata.json b/demonstrations_v2/qnn_module_tf/metadata.json index 15d489d26a..95a5882205 100644 --- a/demonstrations_v2/qnn_module_tf/metadata.json +++ b/demonstrations_v2/qnn_module_tf/metadata.json @@ -6,7 +6,7 @@ } ], "dateOfPublication": "2020-11-02T00:00:00+00:00", - "dateOfLastModification": "2025-03-21T00:00:00+00:00", + "dateOfLastModification": "2025-07-10T00:00:00+00:00", "categories": [ "Devices and Performance", "Quantum Machine Learning" diff --git a/demonstrations_v2/qnspsa/demo.py b/demonstrations_v2/qnspsa/demo.py index d5a6e9f7f5..24df5fecf4 100644 --- a/demonstrations_v2/qnspsa/demo.py +++ b/demonstrations_v2/qnspsa/demo.py @@ -96,7 +96,7 @@ # structure of the non-Euclidean parameter space [#FS]_. The # :math:`d \times d` metric tensor is defined as # -# .. math:: \boldsymbol{g}_{ij}(\mathbf{x}) = -\frac{1}{2} \frac{\partial}{\partial \mathbf{x}_i} \frac{\partial}{\partial \mathbf{x}_j} F(\mathbf{x}', \mathbf{x})\biggr\rvert_{\mathbf{x}'=\mathbf{x}},tag{5} +# .. math:: \boldsymbol{g}_{ij}(\mathbf{x}) = -\frac{1}{2} \frac{\partial}{\partial \mathbf{x}_i} \frac{\partial}{\partial \mathbf{x}_j} F(\mathbf{x}', \mathbf{x})\biggr\rvert_{\mathbf{x}'=\mathbf{x}},\tag{5} # # where # :math:`F(\mathbf{x}', \mathbf{x}) = \bigr\rvert\langle \phi(\mathbf{x}') | \phi(\mathbf{x}) \rangle \bigr\rvert ^ 2,` @@ -357,8 +357,8 @@ def get_grad(params_curr): # def get_overlap_tape(qnode, params1, params2): - tape_forward = qml.workflow.construct_tape(qnode)(*params1) - tape_inv = qml.workflow.construct_tape(qnode)(*params2) + tape_forward = qml.workflow.construct_tape(qnode)(params1) + tape_inv = qml.workflow.construct_tape(qnode)(params2) ops = tape_forward.operations + list(qml.adjoint(op) for op in reversed(tape_inv.operations)) return qml.tape.QuantumTape(ops, [qml.probs(wires=tape_forward.wires)]) @@ -819,8 +819,8 @@ def __get_spsa_grad_tapes(self, cost, params): # used to estimate the gradient per optimization step. The sampled # direction is of the shape of the input parameter. direction = self.__get_perturbation_direction(params) - tape_forward = qml.workflow.construct_tape(cost)(*[params + self.finite_diff_step * direction]) - tape_backward = qml.workflow.construct_tape(cost)(*[params - self.finite_diff_step * direction]) + tape_forward = qml.workflow.construct_tape(cost)(params + self.finite_diff_step * direction) + tape_backward = qml.workflow.construct_tape(cost)(params - self.finite_diff_step * direction) return [tape_forward, tape_backward], direction def __update_tensor(self, tensor_raw): @@ -863,8 +863,8 @@ def __regularize_tensor(self, metric_tensor): def __apply_blocking(self, cost, params_curr, params_next): # For numerical stability: apply the blocking condition on the parameter update. - tape_loss_curr = qml.workflow.construct_tape(cost)(*[params_curr]) - tape_loss_next = qml.workflow.construct_tape(cost)(*[params_next]) + tape_loss_curr = qml.workflow.construct_tape(cost)(params_curr) + tape_loss_next = qml.workflow.construct_tape(cost)(params_next) loss_curr, loss_next = qml.execute([tape_loss_curr, tape_loss_next], cost.device, None) # self.k has been updated earlier. @@ -913,9 +913,9 @@ def __apply_blocking(self, cost, params_curr, params_next): # The optimizer performs reasonably well: the loss drops over optimization # steps and converges finally. We then reproduce the benchmarking test # between the gradient descent, quantum natural gradient descent, SPSA and -# QN-SPSA in Fig. 1(b) of reference [#Gacon2021]_ with the following job. +# QN-SPSA in Fig. 1(b) of reference [#Gacon2021]_ with the following job. # -# .. warning:: +# .. warning:: # The code required to plot the results of the example below is not provided, but a similar # example can be found in this # `notebook `__, diff --git a/demonstrations_v2/qnspsa/metadata.json b/demonstrations_v2/qnspsa/metadata.json index e93a848d74..fb2795e347 100644 --- a/demonstrations_v2/qnspsa/metadata.json +++ b/demonstrations_v2/qnspsa/metadata.json @@ -6,7 +6,7 @@ } ], "dateOfPublication": "2022-07-18T00:00:00+00:00", - "dateOfLastModification": "2025-06-05T00:00:00+00:00", + "dateOfLastModification": "2025-07-11T00:00:00+00:00", "categories": [ "Optimization" ], diff --git a/demonstrations_v2/quantum_volume/demo.py b/demonstrations_v2/quantum_volume/demo.py index 08d3783992..057151eb96 100644 --- a/demonstrations_v2/quantum_volume/demo.py +++ b/demonstrations_v2/quantum_volume/demo.py @@ -15,7 +15,7 @@ *Author: Olivia Di Matteo — Posted: 15 December 2020. Last updated: 15 April 2021.* .. warning:: - The data in this demo was originally generated using system calibration data from the ``ibmq_lima`` device, + The data in this demo was originally generated using system calibration data from the ``ibmq_lima`` device, which has since been retired. While the code now uses a fake version of the Lima device, the noise and calibration may have shifted since the data was generated. @@ -161,6 +161,7 @@ # The median of this probability distribution is: import numpy as np + prob_array = np.fromiter(measurement_probs.values(), dtype=np.float64) print(f"Median = {np.median(prob_array):.3f}") @@ -387,11 +388,10 @@ def qv_circuit_layer(num_qubits): for _ in range(m): qv_circuit_layer(m) -(expanded_tape, ), _ = qml.transforms.decompose(tape, gate_set={qml.QubitUnitary, qml.SWAP}) +(expanded_tape,), _ = qml.transforms.decompose(tape, gate_set={qml.QubitUnitary, qml.SWAP}) print(qml.drawer.tape_text(expanded_tape, wire_order=dev_ideal.wires, show_all_wires=True, show_matrices=True)) - ############################################################################## # .. rst-class:: sphx-glr-script-out # @@ -458,6 +458,7 @@ def qv_circuit_layer(num_qubits): # distribution as follows: # + def heavy_output_set(m, probs): # Compute heavy outputs of an m-qubit circuit with measurement outcome # probabilities given by probs, which is an array with the probabilities @@ -490,7 +491,7 @@ def heavy_output_set(m, probs): # Run the circuit, compute heavy outputs, and print results [output_probs] = qml.execute([tape], dev_ideal) # returns a list of result ! -output_probs = output_probs.reshape(2 ** m, ) +output_probs = output_probs.reshape(2**m) heavy_outputs, prob_heavy_output = heavy_output_set(m, output_probs) print("State\tProbability") @@ -542,8 +543,8 @@ def heavy_output_set(m, probs): # # .. code-block:: python3 # -# from qiskit_ibm_provider import IBMProvider -# IBMProvider.save_account('MY_API_TOKEN') +# from qiskit_ibm_runtime import QiskitRuntimeService +# QiskitRuntimeService.save_account(channel="ibm_quantum_platform", token="", overwrite=True, set_as_default=True) # # A token can be generated by logging into your IBM Q account `here `_ . # @@ -561,13 +562,15 @@ def heavy_output_set(m, probs): from rustworkx.visualization import mpl_draw from qiskit_ibm_runtime.fake_provider import FakeLimaV2 +from matplotlib import pyplot as plt mpl_draw(FakeLimaV2().coupling_map.graph) +plt.show() ############################################################################## # -# .. figure:: ../_static/demonstration_assets/quantum_volume/lima.svg +# .. figure:: ../_static/demonstration_assets/quantum_volume/fake_lima.png # :align: center # :width: 75% # @@ -590,11 +593,11 @@ def heavy_output_set(m, probs): transpile_args = { - "optimization_level": 3, - "coupling_map": FakeLimaV2().coupling_map, - "layout_method": "sabre", - "routing_method": "sabre", - } + "optimization_level": 3, + "coupling_map": FakeLimaV2().coupling_map, + "layout_method": "sabre", + "routing_method": "sabre", +} dev_noisy = qml.device("qiskit.remote", wires=5, shots=1000, backend=FakeLimaV2(), **transpile_args) @@ -624,22 +627,19 @@ def heavy_output_set(m, probs): for _ in range(m): qv_circuit_layer(m) qml.probs(wires=range(m)) - + # when using qml.execute, shots must be on the tape tape_counts = tape_probs.copy(measurements=[qml.counts()], shots=1000) output_probs = qml.execute([tape_probs], dev_ideal) - output_probs = output_probs[0].reshape(2 ** m, ) + output_probs = output_probs[0].reshape(2**m) heavy_outputs, prob_heavy_output = heavy_output_set(m, output_probs) # Execute circuit on the noisy device [counts] = qml.execute([tape_counts], dev_noisy) device_heavy_outputs = np.sum( - [ - counts[x] if x[:m] in heavy_outputs else 0 - for x in counts.keys() - ] + [counts[x] if x[:m] in heavy_outputs else 0 for x in counts.keys()] ) fraction_device_heavy_output = device_heavy_outputs / dev_noisy.shots.total_shots @@ -726,7 +726,6 @@ def heavy_output_set(m, probs): # :math:`2\sigma` away from the threshold! # -from matplotlib import pyplot as plt fig, ax = plt.subplots(2, 2, sharex=True, sharey=True, figsize=(9, 6)) ax = ax.ravel() diff --git a/demonstrations_v2/quantum_volume/metadata.json b/demonstrations_v2/quantum_volume/metadata.json index 4a626d10ed..05e2817552 100644 --- a/demonstrations_v2/quantum_volume/metadata.json +++ b/demonstrations_v2/quantum_volume/metadata.json @@ -6,7 +6,7 @@ } ], "dateOfPublication": "2020-12-15T00:00:00+00:00", - "dateOfLastModification": "2025-01-08T00:00:00+00:00", + "dateOfLastModification": "2025-07-11T00:00:00+00:00", "categories": [ "Quantum Hardware", "Quantum Computing" diff --git a/demonstrations_v2/tutorial_How_to_simulate_quantum_circuits_with_tensor_networks/metadata.json b/demonstrations_v2/tutorial_How_to_simulate_quantum_circuits_with_tensor_networks/metadata.json index 7065e33abc..5a56780fff 100644 --- a/demonstrations_v2/tutorial_How_to_simulate_quantum_circuits_with_tensor_networks/metadata.json +++ b/demonstrations_v2/tutorial_How_to_simulate_quantum_circuits_with_tensor_networks/metadata.json @@ -6,7 +6,7 @@ } ], "dateOfPublication": "2024-07-09T00:00:00+00:00", - "dateOfLastModification": "2024-10-07T00:00:00+00:00", + "dateOfLastModification": "2025-06-25T00:00:00+00:00", "categories": [ "Getting Started", "Quantum Computing", diff --git a/demonstrations_v2/tutorial_QGAN/demo.py b/demonstrations_v2/tutorial_QGAN/demo.py index bc51ac55e2..ff9b6adeba 100644 --- a/demonstrations_v2/tutorial_QGAN/demo.py +++ b/demonstrations_v2/tutorial_QGAN/demo.py @@ -212,9 +212,12 @@ def gen_cost(gen_weights): cost = lambda: disc_cost(disc_weights) for step in range(50): - opt.minimize(cost, [disc_weights]) + with tf.GradientTape() as tape: + loss_value = cost() + gradients = tape.gradient(loss_value, [disc_weights]) + opt.apply_gradients(zip(gradients, [disc_weights])) if step % 5 == 0: - cost_val = cost().numpy() + cost_val = loss_value.numpy() print("Step {}: cost = {}".format(step, cost_val)) @@ -242,9 +245,12 @@ def gen_cost(gen_weights): cost = lambda: gen_cost(gen_weights) for step in range(50): - opt.minimize(cost, [gen_weights]) + with tf.GradientTape() as tape: + loss_value = cost() + gradients = tape.gradient(loss_value, [gen_weights]) + opt.apply_gradients(zip(gradients, [gen_weights])) if step % 5 == 0: - cost_val = cost().numpy() + cost_val = loss_value.numpy() print("Step {}: cost = {}".format(step, cost_val)) diff --git a/demonstrations_v2/tutorial_QGAN/metadata.json b/demonstrations_v2/tutorial_QGAN/metadata.json index 2cf2f49c60..a8a1788db2 100644 --- a/demonstrations_v2/tutorial_QGAN/metadata.json +++ b/demonstrations_v2/tutorial_QGAN/metadata.json @@ -6,7 +6,7 @@ } ], "dateOfPublication": "2019-10-11T00:00:00+00:00", - "dateOfLastModification": "2024-10-07T00:00:00+00:00", + "dateOfLastModification": "2025-06-10T00:00:00+00:00", "categories": [ "Quantum Machine Learning" ], diff --git a/demonstrations_v2/tutorial_adjoint_diff/demo.py b/demonstrations_v2/tutorial_adjoint_diff/demo.py index 5941243c92..3f1506443b 100644 --- a/demonstrations_v2/tutorial_adjoint_diff/demo.py +++ b/demonstrations_v2/tutorial_adjoint_diff/demo.py @@ -71,24 +71,21 @@ # To start, we import PennyLane and Jax's numpy: import pennylane as qml -import jax -from jax import numpy as np - -jax.config.update("jax_platform_name", "cpu") +import pennylane.numpy as pnp ############################################################################## # We also need a circuit to simulate: # -dev = qml.device('default.qubit', wires=2) +dev = qml.device("default.qubit", wires=2) -x = np.array([0.1, 0.2, 0.3]) +x = pnp.array([0.1, 0.2, 0.3], requires_grad=True) @qml.qnode(dev, diff_method="adjoint") def circuit(a): qml.RX(a[0], wires=0) - qml.CNOT(wires=(0,1)) + qml.CNOT(wires=(0, 1)) qml.RY(a[1], wires=1) qml.RZ(a[2], wires=1) return qml.expval(qml.PauliX(wires=1)) @@ -107,20 +104,20 @@ def circuit(a): ops = [ qml.RX(x[0], wires=0), - qml.CNOT(wires=(0,1)), + qml.CNOT(wires=(0, 1)), qml.RY(x[1], wires=1), - qml.RZ(x[2], wires=1) + qml.RZ(x[2], wires=1), ] M = qml.PauliX(wires=1) ############################################################################## # We will be using internal functions to manipulate the nuts and bolts of a statevector # simulation. -# +# # Internally, the statevector simulation uses a 2x2x2x... array to represent the state, whereas # the result of a measurement ``qml.state()`` flattens this internal representation. Each dimension # in the statevector corresponds to a different qubit. -# +# # The internal functions ``create_initial_state`` and ``apply_operation`` # make additional assumptions about their inputs, and will fail or give incorrect results # if those assumptions are not met. To work with these simulation tools, all operations should provide @@ -161,7 +158,7 @@ def circuit(a): # Now we use ``np.vdot`` to take their inner product. ``np.vdot`` sums over all dimensions # and takes the complex conjugate of the first input. -M_expval = np.vdot(bra, ket) +M_expval = pnp.vdot(bra, ket) print("vdot : ", M_expval) print("QNode : ", circuit(x)) @@ -189,10 +186,10 @@ def circuit(a): ket_n = create_initial_state((0, 1)) -for op in ops[:-1]: # don't apply last operation +for op in ops[:-1]: # don't apply last operation ket_n = apply_operation(op, ket_n) -M_expval_n = np.vdot(bra_n, ket_n) +M_expval_n = pnp.vdot(bra_n, ket_n) print(M_expval_n) ############################################################################## @@ -227,7 +224,7 @@ def circuit(a): bra_n_v2 = apply_operation(adj_op, bra_n_v2) ket_n_v2 = apply_operation(adj_op, ket_n_v2) -M_expval_n_v2 = np.vdot(bra_n_v2, ket_n_v2) +M_expval_n_v2 = pnp.vdot(bra_n_v2, ket_n_v2) print(M_expval_n_v2) ############################################################################## @@ -256,7 +253,7 @@ def circuit(a): adj_op = qml.adjoint(op) bra_loop = apply_operation(adj_op, bra_loop) ket_loop = apply_operation(adj_op, ket_loop) - print(np.vdot(bra_loop, ket_loop)) + print(pnp.vdot(bra_loop, ket_loop)) ############################################################################## # Finally to Derivatives! @@ -370,7 +367,7 @@ def circuit(a): dU = qml.operation.operation_derivative(op) ket_temp = apply_operation(qml.QubitUnitary(dU, op.wires), ket) - dM = 2 * np.real(np.vdot(bra, ket_temp)) + dM = 2 * pnp.real(pnp.vdot(bra, ket_temp)) grads.append(dM) bra = apply_operation(adj_op, bra) @@ -382,7 +379,7 @@ def circuit(a): print("our calculation: ", [float(grad) for grad in grads]) -grad_compare = jax.grad(circuit)(x) +grad_compare = qml.grad(circuit)(x) print("comparison: ", grad_compare) ############################################################################## @@ -393,17 +390,17 @@ def circuit(a): # ``"default.qubit"`` or PennyLane's fast C++ simulator ``"lightning.qubit"``. -dev_lightning = qml.device('lightning.qubit', wires=2) +dev_lightning = qml.device("lightning.qubit", wires=2) @qml.qnode(dev_lightning, diff_method="adjoint") def circuit_adjoint(a): qml.RX(a[0], wires=0) - qml.CNOT(wires=(0,1)) + qml.CNOT(wires=(0, 1)) qml.RY(a[1], wires=1) qml.RZ(a[2], wires=1) return qml.expval(M) -print(jax.grad(circuit_adjoint)(x)) +print(qml.grad(circuit_adjoint)(x)) ############################################################################## # Performance diff --git a/demonstrations_v2/tutorial_adjoint_diff/metadata.json b/demonstrations_v2/tutorial_adjoint_diff/metadata.json index 95e9a2c6fc..ab4a089127 100644 --- a/demonstrations_v2/tutorial_adjoint_diff/metadata.json +++ b/demonstrations_v2/tutorial_adjoint_diff/metadata.json @@ -6,7 +6,7 @@ } ], "dateOfPublication": "2021-11-23T00:00:00+00:00", - "dateOfLastModification": "2024-10-07T00:00:00+00:00", + "dateOfLastModification": "2025-07-11T00:00:00+00:00", "categories": [ "Getting Started" ], diff --git a/demonstrations_v2/tutorial_block_encoding/demo.py b/demonstrations_v2/tutorial_block_encoding/demo.py index 22494eaf9e..cc5f74d59b 100644 --- a/demonstrations_v2/tutorial_block_encoding/demo.py +++ b/demonstrations_v2/tutorial_block_encoding/demo.py @@ -104,8 +104,8 @@ # The next step is to identify and prepare the qubit registers used in the oracle access framework. # There are three registers :code:`"ancilla"`, :code:`"wires_i"`, :code:`"wires_j"`. The # :code:`"ancilla"` register will always contain a single qubit, this is the auxilary qubit where we -# apply the rotation gates mentioned above. The :code:`"wires_i"` and :code:`"wires_j"` registers are -# the same size for this algorithm and need to be able to encode :math:`A` itself, so they will both +# apply the rotation gates mentioned above. The :code:`"wires_i"` and :code:`"wires_j"` registers are +# the same size for this algorithm and need to be able to encode :math:`A` itself, so they will both # have :math:`2` qubits for our matrix. ancilla_wires = ["ancilla"] @@ -118,11 +118,8 @@ # Finally, we obtain the control wires for the C-NOT gates and a wire map that we later use to # translate the control wires into the wire registers we prepared. -code = gray_code(2 * np.log2(len(A))) -n_selections = len(code) - -control_wires = [int(np.log2(int(code[i], 2) ^ int(code[(i + 1) % - n_selections], 2))) for i in range(n_selections)] +code = gray_code(int(2 * np.log2(len(A)))) +control_wires = np.log2(code ^ np.roll(code, -1)).astype(int) wire_map = {control_index : wire for control_index, wire in enumerate(wires_j + wires_i)} @@ -236,16 +233,16 @@ def UA(thetas, control_wires, ancilla): # The quantum circuit for the oracle :math:`U_A,` presented above, accesses every entry of # :math:`A` and thus requires :math:`~ O(N^2)` gates to implement the oracle [#fable]_. In the # special cases where :math:`A` is structured and sparse, we can generate a more efficient quantum -# circuit representation for :math:`U_A` and :math:`U_B` [#sparse]_ by only keeping track of the -# non-zero entries of the matrix. +# circuit representation for :math:`U_A` and :math:`U_B` [#sparse]_ by only keeping track of the +# non-zero entries of the matrix. # # Let :math:`b(i,j)` be a function such that it takes a column index :math:`j` and returns the -# row index for the :math:`i^{th}` non-zero entry in that column of :math:`A.` Note, in this -# formulation, the :math:`|i\rangle` qubit register now refers to the number of non-zero entries +# row index for the :math:`i^{th}` non-zero entry in that column of :math:`A.` Note, in this +# formulation, the :math:`|i\rangle` qubit register now refers to the number of non-zero entries # in :math:`A.` For sparse matrices, this can be much smaller than :math:`N,` thus saving us many # qubits. We use this to define :math:`U_A` and :math:`U_B.` # -# Like in the structured approach, the :math:`U_A` oracle is responsible for encoding the matrix +# Like in the structured approach, the :math:`U_A` oracle is responsible for encoding the matrix # entries of :math:`A` into the amplitude of the ancilla qubit. However, we now use :math:`b(i,j)` # to access the row index of the non-zero entries: # @@ -253,7 +250,7 @@ def UA(thetas, control_wires, ancilla): # # U_A |0\rangle_{\text{anc}} |i\rangle |j\rangle = |A_{b(i,j),j}\rangle_{\text{anc}} |i\rangle |j\rangle, # -# In this case the :math:`U_B` oracle is responsible for implementing the :math:`b(i,j)` function +# In this case the :math:`U_B` oracle is responsible for implementing the :math:`b(i,j)` function # and taking us from the column index to the row index in the qubit register: # # .. math:: U_B |i\rangle|j\rangle \ = \ |i\rangle |b(i,j)\rangle @@ -286,7 +283,7 @@ def UA(thetas, control_wires, ancilla): ############################################################################## # Once again we identify and prepare the qubit registers used in the oracle access framework. -# +# # The :code:`"ancilla"` register will still contain a single qubit, the target for the # controlled rotation gates. The :code:`"wires_i"` register needs to be large enough to binary # encode the maximum number of non-zero entries in any column or row. Given the structure of diff --git a/demonstrations_v2/tutorial_block_encoding/metadata.json b/demonstrations_v2/tutorial_block_encoding/metadata.json index f9f3ca3656..1be9d62bfd 100644 --- a/demonstrations_v2/tutorial_block_encoding/metadata.json +++ b/demonstrations_v2/tutorial_block_encoding/metadata.json @@ -12,7 +12,7 @@ } ], "dateOfPublication": "2023-11-28T00:00:00+00:00", - "dateOfLastModification": "2025-05-27T00:00:00+00:00", + "dateOfLastModification": "2025-06-02T00:00:00+00:00", "categories": [ "Quantum Computing", "Algorithms" diff --git a/demonstrations_v2/tutorial_error_mitigation/requirements.in b/demonstrations_v2/tutorial_error_mitigation/requirements.in index 7faec58028..ec9119f2eb 100644 --- a/demonstrations_v2/tutorial_error_mitigation/requirements.in +++ b/demonstrations_v2/tutorial_error_mitigation/requirements.in @@ -1,9 +1,3 @@ -mitiq==0.32.0 +mitiq==0.43.0 ply==3.11 -qiskit -qiskit_aer -qiskit_ibm_runtime -pennylane-cirq -pennylane-qiskit -jax -jaxlib +pennylane_qiskit diff --git a/demonstrations_v2/tutorial_iqp_circuit_optimization_jax/demo.py b/demonstrations_v2/tutorial_iqp_circuit_optimization_jax/demo.py index e46309b9b0..821875da0d 100644 --- a/demonstrations_v2/tutorial_iqp_circuit_optimization_jax/demo.py +++ b/demonstrations_v2/tutorial_iqp_circuit_optimization_jax/demo.py @@ -134,11 +134,12 @@ def penn_iqp_gates(params: np.ndarray, gates: list, n_qubits: int): for i in range(n_qubits): qml.Hadamard(i) + ###################################################################### # Now we have our circuit, we can evaluate expectation values of tensor products of Pauli Z operators # specified by lists of the form ``op`` above. # -def penn_obs(op: np.ndarray) -> qml.operation.Observable: +def penn_obs(op: np.ndarray) -> qml.operation.Operator: """Returns a PennyLane observable from a bitstring representation. Args: @@ -158,6 +159,7 @@ def penn_obs(op: np.ndarray) -> qml.operation.Observable: obs @= qml.Z(i) return obs + def penn_iqp_circuit(params: np.ndarray, gates: list, op: np.ndarray, n_qubits: int) -> qml.measurements.ExpectationMP: """Defines the circuit that calculates the expectation value of the operator with the IQP circuit with PennyLane tools. @@ -594,22 +596,22 @@ def loss_fn(params, circuit, ops, n_samples, key): # # .. [#nest] # -# M. Van den Nest. +# M. Van den Nest. # "Simulating quantum computers with probabilistic methods" # `arXiv:0911.1624 `__, 2010. # # .. [#gretton] # -# Arthur Gretton, Karsten M. Borgwardt, Malte J. Rasch, Bernhard Schölkopf, Alexander Smola. +# Arthur Gretton, Karsten M. Borgwardt, Malte J. Rasch, Bernhard Schölkopf, Alexander Smola. # "A Kernel Two-Sample Test" # `http://jmlr.org/papers/v13/gretton12a.html `__, in Journal of Machine Learning Research 13.25, pp. 723-773, 2012. # # .. [#ravuri] # -# Suman Ravuri, Mélanie Rey, Shakir Mohamed, Marc Peter Deisenroth. +# Suman Ravuri, Mélanie Rey, Shakir Mohamed, Marc Peter Deisenroth. # "Understanding Deep Generative Models with Generalized Empirical Likelihoods" # `arXiv:2306.09780 `__, 2023. -# +# # About the authors # ---------------- -# +# diff --git a/demonstrations_v2/tutorial_iqp_circuit_optimization_jax/metadata.json b/demonstrations_v2/tutorial_iqp_circuit_optimization_jax/metadata.json index f045cffd4e..c65fc9b253 100644 --- a/demonstrations_v2/tutorial_iqp_circuit_optimization_jax/metadata.json +++ b/demonstrations_v2/tutorial_iqp_circuit_optimization_jax/metadata.json @@ -9,7 +9,7 @@ } ], "dateOfPublication": "2025-02-14T09:00:00+00:00", - "dateOfLastModification": "2025-02-14T09:00:00+00:00", + "dateOfLastModification": "2025-06-02T09:00:00+00:00", "categories": [ "Optimization", "Algorithms" diff --git a/demonstrations_v2/tutorial_kak_decomposition/demo.py b/demonstrations_v2/tutorial_kak_decomposition/demo.py index 8a61aa3cc9..2d341a1b41 100644 --- a/demonstrations_v2/tutorial_kak_decomposition/demo.py +++ b/demonstrations_v2/tutorial_kak_decomposition/demo.py @@ -348,12 +348,13 @@ def is_orthogonal(op, basis): # # Let us define it in code, and check whether it gives rise to a Cartan decomposition. # As we want to look at another example later, we wrap everything in a function. +# A similar function is available in PennyLane as :func:`~.pennylane.liealg.check_cartan_decomp`. # -def check_cartan_decomposition(g, k, space_name): +def check_cartan_decomp(g, k, space_name): """Given an algebra g and an operator subspace k, verify that k is a subalgebra - and gives rise to a Cartan decomposition.""" + and gives rise to a Cartan decomposition. Similar to qml.liealg.check_cartan_decomp""" # Check Lie closure of k k_lie_closure = qml.lie_closure(k) k_is_closed = len(k_lie_closure) == len(k) @@ -387,7 +388,7 @@ def check_cartan_decomposition(g, k, space_name): u1 = [Z(0)] space_name = "SU(2)/U(1)" -p = check_cartan_decomposition(su2, u1, space_name) +p = check_cartan_decomp(su2, u1, space_name) ###################################################################### # Cartan subalgebras @@ -811,7 +812,7 @@ def theta_Y(x): # Define subalgebra su(2) ⊕ su(2) su2_su2 = [X(0), Y(0), Z(0), X(1), Y(1), Z(1)] space_name = "SU(4)/(SU(2)xSU(2))" -p = check_cartan_decomposition(su4, su2_su2, space_name) +p = check_cartan_decomp(su4, su2_su2, space_name) ###################################################################### # .. admonition:: Math detail: involution for two-qubit decomposition diff --git a/demonstrations_v2/tutorial_kak_decomposition/metadata.json b/demonstrations_v2/tutorial_kak_decomposition/metadata.json index ae7f442f81..0c1bec711c 100644 --- a/demonstrations_v2/tutorial_kak_decomposition/metadata.json +++ b/demonstrations_v2/tutorial_kak_decomposition/metadata.json @@ -6,7 +6,7 @@ } ], "dateOfPublication": "2024-11-25T00:00:00+00:00", - "dateOfLastModification": "2025-03-17T09:00:00+00:00", + "dateOfLastModification": "2025-03-26T09:00:00+00:00", "categories": [ "Quantum Computing", "Algorithms" diff --git a/demonstrations_v2/tutorial_liesim_extension/demo.py b/demonstrations_v2/tutorial_liesim_extension/demo.py index ce62806eb9..220702874e 100644 --- a/demonstrations_v2/tutorial_liesim_extension/demo.py +++ b/demonstrations_v2/tutorial_liesim_extension/demo.py @@ -100,8 +100,8 @@ def TFIM(n): generators = [op.pauli_rep for op in generators] dla = qml.lie_closure(generators, pauli=True) - dim_dla = len(dla) - return generators, dla, dim_dla + dim_g = len(dla) + return generators, dla, dim_g generators, dla, dim_g = TFIM(n=4) diff --git a/demonstrations_v2/tutorial_liesim_extension/metadata.json b/demonstrations_v2/tutorial_liesim_extension/metadata.json index 3837196b18..1807c7b04a 100644 --- a/demonstrations_v2/tutorial_liesim_extension/metadata.json +++ b/demonstrations_v2/tutorial_liesim_extension/metadata.json @@ -6,7 +6,7 @@ } ], "dateOfPublication": "2024-06-18T00:00:00+00:00", - "dateOfLastModification": "2025-03-17T00:00:00+00:00", + "dateOfLastModification": "2025-03-26T00:00:00+00:00", "categories": [ "Quantum Computing", "Quantum Machine Learning" diff --git a/demonstrations_v2/tutorial_odegen/demo.py b/demonstrations_v2/tutorial_odegen/demo.py index 8894ff68d8..11577b6e82 100644 --- a/demonstrations_v2/tutorial_odegen/demo.py +++ b/demonstrations_v2/tutorial_odegen/demo.py @@ -296,7 +296,7 @@ def partial_step(grad_circuit, opt_state, theta): return thetas, energy -key = jax.random.PRNGKey(0) +key = jax.random.PRNGKey(888) theta0 = jax.random.normal(key, shape=(n_param_batch, tbins * 2)) thetaf_odegen, energy_odegen = run_opt(value_and_grad_jax, theta0) @@ -313,8 +313,9 @@ def partial_step(grad_circuit, opt_state, theta): ############################################################################## # We see that with analytic gradients (ODEgen), we can reach the ground state energy within 100 epochs, whereas with SPS gradients we cannot find the path -# towards the minimum due to the stochasticity of the gradient estimates. Note that both optimizations start from the same (random) initial point. -# This picture solidifies when repeating this procedure for multiple runs from different random initializations, as was demonstrated in [#Kottmann]_. +# towards the minimum due to the stochasticity of the gradient estimates. Note that the convergence of the optimization is sensitive to the initial guess. +# In this demonstration, both optimizations start from the same (random) initial point. This picture solidifies when repeating this procedure for multiple +# runs from different random initializations, as was demonstrated in [#Kottmann]_. # # We also want to make sure that this is a fair comparison in terms of quantum resources. In the case of ODEgen, we maximally have :math:`\mathcal{R}_\text{ODEgen} = 2 (4^n - 1) = 30` expectation values. # For SPS we have :math:`2 N_g N_s = 32` (due to :math:`N_g = 2` and :math:`N_s=8` time samples per gradient that we chose in ``num_split_times`` above). Thus, overall, we require fewer diff --git a/demonstrations_v2/tutorial_odegen/metadata.json b/demonstrations_v2/tutorial_odegen/metadata.json index df0ad04903..a84d59773b 100644 --- a/demonstrations_v2/tutorial_odegen/metadata.json +++ b/demonstrations_v2/tutorial_odegen/metadata.json @@ -6,7 +6,7 @@ } ], "dateOfPublication": "2023-12-12T00:00:00+00:00", - "dateOfLastModification": "2025-01-28T00:00:00+00:00", + "dateOfLastModification": "2025-07-08T00:00:00+00:00", "categories": [ "Optimization", "Quantum Computing", diff --git a/demonstrations_v2/tutorial_pasqal/demo.py b/demonstrations_v2/tutorial_pasqal/demo.py index b49898f392..5bc8ae70e5 100644 --- a/demonstrations_v2/tutorial_pasqal/demo.py +++ b/demonstrations_v2/tutorial_pasqal/demo.py @@ -320,10 +320,13 @@ def cost(): output = (-circuit(weights, data) + 1) / 2 return tf.abs(output - label) ** 2 -opt = tf.keras.optimizers.Adam(learning_rate=0.1) +opt = tf.optimizers.Adam(learning_rate=0.1) for step in range(100): - opt.minimize(cost, [weights]) + with tf.GradientTape() as tape: + loss_value = cost() + gradients = tape.gradient(loss_value, [weights]) + opt.apply_gradients(zip(gradients, [weights])) if step % 5 == 0: print("Step {}: cost={}".format(step, cost())) diff --git a/demonstrations_v2/tutorial_pasqal/metadata.json b/demonstrations_v2/tutorial_pasqal/metadata.json index f5decd0ac3..bcafc117f7 100644 --- a/demonstrations_v2/tutorial_pasqal/metadata.json +++ b/demonstrations_v2/tutorial_pasqal/metadata.json @@ -6,7 +6,7 @@ } ], "dateOfPublication": "2020-10-13T00:00:00+00:00", - "dateOfLastModification": "2024-10-07T00:00:00+00:00", + "dateOfLastModification": "2025-06-10T00:00:00+00:00", "categories": [ "Quantum Hardware", "Quantum Computing" diff --git a/demonstrations_v2/tutorial_univariate_qvr/demo.py b/demonstrations_v2/tutorial_univariate_qvr/demo.py index 38966da266..14a4e46b7f 100644 --- a/demonstrations_v2/tutorial_univariate_qvr/demo.py +++ b/demonstrations_v2/tutorial_univariate_qvr/demo.py @@ -233,16 +233,10 @@ import covalent as ct import os import time -import subprocess -import sys -from pathlib import Path # Set up Covalent server os.environ["COVALENT_SERVER_IFACE_ANY"] = "1" -subprocess.run( - ["covalent", "start"], - env=os.environ).check_returncode() - +os.system("covalent start") # If you run into any out-of-memory issues with Dask when running this notebook, # Try reducing the number of workers and making a specific memory request. I.e.: # os.system("covalent start -m "2GiB" -n 2") @@ -840,9 +834,6 @@ def training_workflow( # ct_tr_results = ct.get_result(dispatch_id=tr_dispatch_id, wait=True) -if ct_tr_results.error: - raise RuntimeError(ct_tr_results.error) - results_dict = ct_tr_results.result @@ -1221,9 +1212,7 @@ def testing_workflow( # # Shut down the covalent server -subprocess.run( - ["covalent", "stop"], - env=os.environ).check_returncode() +stop = os.system("covalent stop") ###################################################################### # Conclusions diff --git a/demonstrations_v2/tutorial_zne_catalyst/requirements.in b/demonstrations_v2/tutorial_zne_catalyst/requirements.in deleted file mode 100644 index 0d13aac4ca..0000000000 --- a/demonstrations_v2/tutorial_zne_catalyst/requirements.in +++ /dev/null @@ -1,3 +0,0 @@ -pennylane-catalyst -pennylane-qrack -pyqrack diff --git a/demonstrations_v2/tutorial_zne_catalyst/demo.py b/demonstrations_v2/zne_catalyst/demo.py similarity index 100% rename from demonstrations_v2/tutorial_zne_catalyst/demo.py rename to demonstrations_v2/zne_catalyst/demo.py diff --git a/demonstrations_v2/tutorial_zne_catalyst/metadata.json b/demonstrations_v2/zne_catalyst/metadata.json similarity index 100% rename from demonstrations_v2/tutorial_zne_catalyst/metadata.json rename to demonstrations_v2/zne_catalyst/metadata.json diff --git a/demonstrations_v2/zne_catalyst/requirements.in b/demonstrations_v2/zne_catalyst/requirements.in new file mode 100644 index 0000000000..e69de29bb2 diff --git a/dependencies/constraints-dev.txt b/dependencies/constraints-dev.txt index 8623e624fb..a5bbb5cc19 100644 --- a/dependencies/constraints-dev.txt +++ b/dependencies/constraints-dev.txt @@ -3,7 +3,8 @@ --extra-index-url https://test.pypi.org/simple/ --extra-index-url https://download.pytorch.org/whl/cpu -pennylane @ git+https://github.com/PennyLaneAI/pennylane.git#egg=pennylane +pennylane @ git+https://github.com/PennyLaneAI/pennylane.git@v0.42.0-rc0#egg=pennylane +pennylane-cirq @ git+https://github.com/PennyLaneAI/pennylane-cirq.git#egg=pennylane-cirq pennylane-qiskit @ git+https://github.com/PennyLaneAI/pennylane-qiskit.git#egg=pennylane-qiskit pennylane-qulacs @ git+https://github.com/PennyLaneAI/pennylane-qulacs.git#egg=pennylane-qulacs iqpot @ git+https://github.com/XanaduAI/iqpopt.git#egg=iqpopt @@ -11,13 +12,15 @@ pennylane-catalyst pennylane-lightning chex<1.0.0 +cirq-core==1.4.1 pyqrack==1.32.12 numpy~=1.24 matplotlib==3.7.2 jax==0.4.28 jaxlib==0.4.28 -jaxopt==0.8.3 +jaxopt==0.8.4 aiohttp==3.9.5 +fastapi<1.0.0 fsspec==2024.6.1 h5py==3.11.0 openfermionpyscf==0.5 @@ -29,7 +32,8 @@ torch==2.1.2+cpu ; sys_platform != 'darwin' torch==2.1.2 ; sys_platform == 'darwin' torchvision==0.16.2+cpu ; sys_platform != 'darwin' torchvision==0.16.2 ; sys_platform == 'darwin' -tensorflow==2.14.1 +tensorflow==2.16.2 +typing-extensions==4.14.0 optax==0.2.3 quimb==1.8.2 kahypar==1.1.7 diff --git a/lib/qml/app/app.py b/lib/qml/app/app.py index 115aa93059..70f7ce700d 100644 --- a/lib/qml/app/app.py +++ b/lib/qml/app/app.py @@ -144,7 +144,7 @@ def sync_v2(): shutil.copy2(v1_metadata, v2_metadata) print( - f"Copied new demo {v1_demo} to {v2_demo_dir}. Please updated the requirements.in file." + f"Copied new demo {v1_demo} to {v2_demo_dir}. Please update the requirements.in file." ) else: for src, dest in [(v1_demo, v2_demo), (v1_metadata, v2_metadata)]: diff --git a/lib/qml/lib/cmds.py b/lib/qml/lib/cmds.py index 5b563bff84..6a040bd688 100644 --- a/lib/qml/lib/cmds.py +++ b/lib/qml/lib/cmds.py @@ -62,7 +62,7 @@ def pip_install( requirements: Path to a requirements file constraints: Path to a constriants file quiet: Whether to suppress output to stdout - pre: Include pre-release versions of pacakges + pre: Include pre-release versions of packages Raises: diff --git a/lib/qml/lib/demo.py b/lib/qml/lib/demo.py index ffb7fd49a8..85366627b4 100644 --- a/lib/qml/lib/demo.py +++ b/lib/qml/lib/demo.py @@ -162,7 +162,7 @@ def build( build_venv = Virtualenv(ctx.build_venv_path) cmds.pip_install( - build_venv.python, requirements=ctx.build_requirements_file, use_uv=False + build_venv.python, requirements=ctx.build_requirements_file, use_uv=False, quiet=False ) for demo in demos: @@ -245,6 +245,7 @@ def generate_requirements( output_file, *requirements_in, constraints_files=constraints, + quiet=False, prerelease=dev, ) @@ -268,9 +269,86 @@ def _build_demo( build_venv.python, "--upgrade", requirements=out_dir / "requirements.txt", - quiet=True, + quiet=False, pre=dev, ) + + # For dev, follow the same install procedure and order as in the Makefile. + # This is critical to get the proper versions of PennyLane, Catalyst, + # and various plugins. + # TODO: See if we can clean this up and streamline in the future... + # TODO: Remove RC branch for PennyLane install post-release. + if dev and execute: + # Cirq + cmds.pip_install( + build_venv.python, + "--upgrade", + "git+https://github.com/PennyLaneAI/pennylane-cirq.git#egg=pennylane-cirq", + use_uv=False, + quiet=False, + ) + # Qiskit + cmds.pip_install( + build_venv.python, + "--upgrade", + "git+https://github.com/PennyLaneAI/pennylane-qiskit.git#egg=pennylane-qiskit", + use_uv=False, + quiet=False, + ) + # Qulacs + cmds.pip_install( + build_venv.python, + "--upgrade", + "git+https://github.com/PennyLaneAI/pennylane-qulacs.git#egg=pennylane-qulacs", + use_uv=False, + quiet=False, + ) + # Catalyst + cmds.pip_install( + build_venv.python, + "--upgrade", + "--extra-index-url", + "https://test.pypi.org/simple/", + "PennyLane-Catalyst", + use_uv=False, + quiet=False, + pre=True, + ) + # Lightning + cmds.pip_install( + build_venv.python, + "--upgrade", + "--extra-index-url", + "https://test.pypi.org/simple/", + "PennyLane-Lightning", + use_uv=False, + quiet=False, + pre=True, + ) + # PennyLane + cmds.pip_install( + build_venv.python, + "--upgrade", + "git+https://github.com/PennyLaneAI/pennylane.git@v0.42.0-rc0#egg=pennylane", + use_uv=False, + quiet=False, + ) + # Iqpopt + cmds.pip_install( + build_venv.python, + "--upgrade", + "git+https://github.com/XanaduAI/iqpopt.git#egg=iqpopt", + use_uv=False, + quiet=False, + ) + # We need to bump flax here, after Jax has been bumped by Catalyst + cmds.pip_install( + build_venv.python, + "--upgrade", + "flax==0.10.6", + use_uv=False, + quiet=False, + ) stage_dir = ctx.build_dir / "demonstrations" fs.clean_dir(stage_dir) diff --git a/poetry.lock b/poetry.lock index 28103c2499..b3b708c0a4 100644 --- a/poetry.lock +++ b/poetry.lock @@ -1,15 +1,15 @@ -# This file is automatically @generated by Poetry 2.1.1 and should not be changed by hand. +# This file is automatically @generated by Poetry 2.1.3 and should not be changed by hand. [[package]] name = "absl-py" -version = "2.3.0" +version = "2.3.1" description = "Abseil Python Common Libraries, see https://github.com/abseil/abseil-py." optional = false python-versions = ">=3.8" groups = ["executable-dependencies"] files = [ - {file = "absl_py-2.3.0-py3-none-any.whl", hash = "sha256:9824a48b654a306168f63e0d97714665f8490b8d89ec7bf2efc24bf67cf579b3"}, - {file = "absl_py-2.3.0.tar.gz", hash = "sha256:d96fda5c884f1b22178852f30ffa85766d50b99e00775ea626c23304f582fc4f"}, + {file = "absl_py-2.3.1-py3-none-any.whl", hash = "sha256:eeecf07f0c2a93ace0772c92e596ace6d3d3996c042b2128459aaae2a76de11d"}, + {file = "absl_py-2.3.1.tar.gz", hash = "sha256:a97820526f7fbfd2ec1bce83f3f25e3a14840dac0d8e02a0b71cd75db3f77fc9"}, ] [[package]] @@ -123,18 +123,19 @@ speedups = ["Brotli ; platform_python_implementation == \"CPython\"", "aiodns ; [[package]] name = "aiosignal" -version = "1.3.2" +version = "1.4.0" description = "aiosignal: a list of registered asynchronous callbacks" optional = 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