diff --git a/demonstrations_v2/adjoint_diff_benchmarking/metadata.json b/demonstrations_v2/adjoint_diff_benchmarking/metadata.json index f2aedddbe1..6639c47149 100644 --- a/demonstrations_v2/adjoint_diff_benchmarking/metadata.json +++ b/demonstrations_v2/adjoint_diff_benchmarking/metadata.json @@ -8,7 +8,7 @@ "executable_stable": false, "executable_latest": false, "dateOfPublication": "2021-11-23T00:00:00+00:00", - "dateOfLastModification": "2025-09-09T17:41:50+00:00", + "dateOfLastModification": "2025-09-22T15:48:14+00:00", "categories": [ "Getting Started" ], diff --git a/demonstrations_v2/ahs_aquila/demo.py b/demonstrations_v2/ahs_aquila/demo.py index 9c6c118a9f..446e349a4e 100644 --- a/demonstrations_v2/ahs_aquila/demo.py +++ b/demonstrations_v2/ahs_aquila/demo.py @@ -57,7 +57,7 @@ more information on pulse programming in PennyLane, see the `PennyLane docs `__, or check out the demo about -`running a ctrl-VQE algorithm with pulse control `__ on the PennyLane `default.qubit` simulator. +:doc:`running a ctrl-VQE algorithm with pulse control ` on the PennyLane `default.qubit` simulator. @@ -66,7 +66,7 @@ The Aquila QPU works with programmable arrays of up to 256 Rubidium-87 atoms (Rb-87), trapped in vacuum by tightly focused laser beams. These atoms can be arranged in (almost) -`arbitrary user-specified geometries `_ to determine +:doc:`arbitrary user-specified geometries ` to determine inter-qubit interactions. On the Aquila device, it is possible to specify 1D and 2D atom arrangements. Atom positions may be slightly shifted to accommodate hardware limitations, and must obey lattice constraints for spacing. This will be explored in more detail below. diff --git a/demonstrations_v2/ahs_aquila/metadata.json b/demonstrations_v2/ahs_aquila/metadata.json index fdf85b11d8..995f0e3287 100644 --- a/demonstrations_v2/ahs_aquila/metadata.json +++ b/demonstrations_v2/ahs_aquila/metadata.json @@ -8,7 +8,7 @@ "executable_stable": false, "executable_latest": false, "dateOfPublication": "2023-05-16T00:00:00+00:00", - "dateOfLastModification": "2025-09-09T17:41:50+00:00", + "dateOfLastModification": "2025-09-22T15:48:14+00:00", "categories": [ "Quantum Hardware", "Devices and Performance", diff --git a/demonstrations_v2/braket-parallel-gradients/metadata.json b/demonstrations_v2/braket-parallel-gradients/metadata.json index babe860693..cc3960822e 100644 --- a/demonstrations_v2/braket-parallel-gradients/metadata.json +++ b/demonstrations_v2/braket-parallel-gradients/metadata.json @@ -14,7 +14,7 @@ "executable_stable": false, "executable_latest": false, "dateOfPublication": "2020-12-08T00:00:00+00:00", - "dateOfLastModification": "2025-09-09T17:41:50+00:00", + "dateOfLastModification": "2025-09-22T15:48:14+00:00", "categories": [ "Devices and Performance" ], diff --git a/demonstrations_v2/circuits_as_fourier_series/metadata.json b/demonstrations_v2/circuits_as_fourier_series/metadata.json index 2a7c873389..c85ca7c35a 100644 --- a/demonstrations_v2/circuits_as_fourier_series/metadata.json +++ b/demonstrations_v2/circuits_as_fourier_series/metadata.json @@ -8,7 +8,7 @@ "executable_stable": false, "executable_latest": false, "dateOfPublication": "2023-09-11T00:00:00+00:00", - "dateOfLastModification": "2025-09-17T17:41:50+00:00", + "dateOfLastModification": "2025-09-22T15:48:14+00:00", "categories": [ "Optimization", "Quantum Machine Learning" diff --git a/demonstrations_v2/covalent_cloud_gpu/metadata.json b/demonstrations_v2/covalent_cloud_gpu/metadata.json index 380723dcc2..b67c130cef 100644 --- a/demonstrations_v2/covalent_cloud_gpu/metadata.json +++ b/demonstrations_v2/covalent_cloud_gpu/metadata.json @@ -11,7 +11,7 @@ "executable_stable": false, "executable_latest": false, "dateOfPublication": "2024-05-24T00:00:00+00:00", - "dateOfLastModification": "2025-09-09T17:41:50+00:00", + "dateOfLastModification": "2025-09-22T15:48:14+00:00", "categories": [ "Quantum Machine Learning" ], diff --git a/demonstrations_v2/ensemble_multi_qpu/demo.py b/demonstrations_v2/ensemble_multi_qpu/demo.py index f1dfc1a275..74bcfc65f9 100644 --- a/demonstrations_v2/ensemble_multi_qpu/demo.py +++ b/demonstrations_v2/ensemble_multi_qpu/demo.py @@ -46,7 +46,7 @@ ############################################################################## # This tutorial requires the ``pennylane-rigetti`` and ``pennylane-qiskit`` packages, which can be -# installed by following the instructions `here `__. We also +# installed by following the instructions `here `__. We also # make use of the `PyTorch interface `_, which can be installed from `here # `__. diff --git a/demonstrations_v2/ensemble_multi_qpu/metadata.json b/demonstrations_v2/ensemble_multi_qpu/metadata.json index 1a9fdfa9a4..04b87a5600 100644 --- a/demonstrations_v2/ensemble_multi_qpu/metadata.json +++ b/demonstrations_v2/ensemble_multi_qpu/metadata.json @@ -8,7 +8,7 @@ "executable_stable": false, "executable_latest": false, "dateOfPublication": "2020-02-14T00:00:00+00:00", - "dateOfLastModification": "2025-09-09T17:41:50+00:00", + "dateOfLastModification": "2025-09-22T15:48:14+00:00", "categories": [ "Quantum Machine Learning" ], diff --git a/demonstrations_v2/function_fitting_qsp/demo.py b/demonstrations_v2/function_fitting_qsp/demo.py index 0f0b72168f..f9256af8d3 100644 --- a/demonstrations_v2/function_fitting_qsp/demo.py +++ b/demonstrations_v2/function_fitting_qsp/demo.py @@ -553,7 +553,7 @@ def step_func(x): # explicitly computing the optimal values for :math:`\vec{\phi}` # known as "Remez-type exchange algorithms" for analytic function fitting. If # you want to explore other approaches to function fitting, checkout this -# `demo `__ +# :doc:`demo ` # where we use a photonic neural network for function fitting. # # diff --git a/demonstrations_v2/function_fitting_qsp/metadata.json b/demonstrations_v2/function_fitting_qsp/metadata.json index 7b42f25c57..9ae7fc42cb 100644 --- a/demonstrations_v2/function_fitting_qsp/metadata.json +++ b/demonstrations_v2/function_fitting_qsp/metadata.json @@ -8,7 +8,7 @@ "executable_stable": false, "executable_latest": false, "dateOfPublication": "2022-05-24T00:00:00+00:00", - "dateOfLastModification": "2025-09-09T17:41:50+00:00", + "dateOfLastModification": "2025-09-22T15:48:14+00:00", "categories": [ "Quantum Machine Learning" ], diff --git a/demonstrations_v2/gbs/metadata.json b/demonstrations_v2/gbs/metadata.json index b9f7340f64..5416110961 100644 --- a/demonstrations_v2/gbs/metadata.json +++ b/demonstrations_v2/gbs/metadata.json @@ -11,7 +11,7 @@ "executable_stable": false, "executable_latest": false, "dateOfPublication": "2020-12-04T00:00:00+00:00", - "dateOfLastModification": "2025-09-09T17:41:50+00:00", + "dateOfLastModification": "2025-09-22T15:48:14+00:00", "categories": [ "Quantum Hardware", "Quantum Computing" diff --git a/demonstrations_v2/getting_started_with_hybrid_jobs/metadata.json b/demonstrations_v2/getting_started_with_hybrid_jobs/metadata.json index f12a3e95b6..455c61d981 100644 --- a/demonstrations_v2/getting_started_with_hybrid_jobs/metadata.json +++ b/demonstrations_v2/getting_started_with_hybrid_jobs/metadata.json @@ -8,7 +8,7 @@ "executable_stable": false, "executable_latest": false, "dateOfPublication": "2023-10-16T00:00:00+00:00", - "dateOfLastModification": "2025-09-09T17:41:50+00:00", + "dateOfLastModification": "2025-09-22T15:48:14+00:00", "categories": [ "Devices and Performance" ], diff --git a/demonstrations_v2/gqe_training/metadata.json b/demonstrations_v2/gqe_training/metadata.json index 3690f0e72a..87e78f2da5 100644 --- a/demonstrations_v2/gqe_training/metadata.json +++ b/demonstrations_v2/gqe_training/metadata.json @@ -11,7 +11,7 @@ "executable_stable": false, "executable_latest": false, "dateOfPublication": "2024-09-20T00:00:00+00:00", - "dateOfLastModification": "2025-09-09T17:41:50+00:00", + "dateOfLastModification": "2025-09-22T15:48:14+00:00", "categories": [ "Quantum Machine Learning", "Quantum Chemistry", diff --git a/demonstrations_v2/how_to_catalyst_lightning_gpu/metadata.json b/demonstrations_v2/how_to_catalyst_lightning_gpu/metadata.json index 621a3dd27e..15d93bfd9b 100644 --- a/demonstrations_v2/how_to_catalyst_lightning_gpu/metadata.json +++ b/demonstrations_v2/how_to_catalyst_lightning_gpu/metadata.json @@ -11,7 +11,7 @@ "executable_stable": false, "executable_latest": false, "dateOfPublication": "2025-02-21T10:00:00+00:00", - "dateOfLastModification": "2025-09-09T17:41:50+00:00", + "dateOfLastModification": "2025-09-22T15:48:14+00:00", "categories": [ "Getting Started", "Quantum Chemistry", 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 33c4dfa9e4..d01ea28bfe 100644 --- a/demonstrations_v2/how_to_use_qiskit1_with_pennylane/metadata.json +++ b/demonstrations_v2/how_to_use_qiskit1_with_pennylane/metadata.json @@ -8,7 +8,7 @@ "executable_stable": false, "executable_latest": false, "dateOfPublication": "2024-07-02T00:00:00+00:00", - "dateOfLastModification": "2025-09-09T17:41:50+00:00", + "dateOfLastModification": "2025-09-22T15:48:14+00:00", "categories": [ "Quantum Computing", "How-to" diff --git a/demonstrations_v2/ibm_pennylane/metadata.json b/demonstrations_v2/ibm_pennylane/metadata.json index 1a77749e9e..200b28a8f1 100644 --- a/demonstrations_v2/ibm_pennylane/metadata.json +++ b/demonstrations_v2/ibm_pennylane/metadata.json @@ -14,7 +14,7 @@ "executable_stable": false, "executable_latest": false, "dateOfPublication": "2023-06-20T00:00:00+00:00", - "dateOfLastModification": "2025-09-09T17:41:50+00:00", + "dateOfLastModification": "2025-09-22T15:48:14+00:00", "categories": [ "Devices and Performance" ], diff --git a/demonstrations_v2/learning2learn/demo.py b/demonstrations_v2/learning2learn/demo.py index 64ab8d110d..768dc987a3 100644 --- a/demonstrations_v2/learning2learn/demo.py +++ b/demonstrations_v2/learning2learn/demo.py @@ -119,7 +119,7 @@ ansatz, and such a quantum circuit is used to evaluate the cost Hamiltonian :math:`H` of the MaxCut problem. You can check out a great tutorial on -[how to use QAOA for solving graph problems](https://pennylane.ai/qml/demos/tutorial_qaoa_intro.html). +:doc:`how to use QAOA for solving graph problems `. .. note:: Running the tutorial (excluding the Appendix) requires approx. ~13m. @@ -783,7 +783,7 @@ def train_step(graph_cost): # # .. [#maxcut] # -# MaxCut problem: `https://pennylane.ai/qml/demos/tutorial_qaoa_maxcut/ `__. +# MaxCut problem: :doc:`demos/tutorial_qaoa_maxcut`. # # # diff --git a/demonstrations_v2/learning2learn/metadata.json b/demonstrations_v2/learning2learn/metadata.json index 48875daed2..7a83eb6cca 100644 --- a/demonstrations_v2/learning2learn/metadata.json +++ b/demonstrations_v2/learning2learn/metadata.json @@ -8,7 +8,7 @@ "executable_stable": false, "executable_latest": false, "dateOfPublication": "2021-03-02T00:00:00+00:00", - "dateOfLastModification": "2025-09-09T17:41:50+00:00", + "dateOfLastModification": "2025-09-22T15:48:14+00:00", "categories": [ "Quantum Machine Learning" ], diff --git a/demonstrations_v2/linear_equations_hhl_qrisp_catalyst/metadata.json b/demonstrations_v2/linear_equations_hhl_qrisp_catalyst/metadata.json index 192f6205bd..b39e2a6d54 100644 --- a/demonstrations_v2/linear_equations_hhl_qrisp_catalyst/metadata.json +++ b/demonstrations_v2/linear_equations_hhl_qrisp_catalyst/metadata.json @@ -14,7 +14,7 @@ "executable_stable": false, "executable_latest": false, "dateOfPublication": "2025-02-26T09:00:00+00:00", - "dateOfLastModification": "2025-09-09T17:41:50+00:00", + "dateOfLastModification": "2025-09-22T15:48:14+00:00", "categories": [ "Algorithms", "Quantum Computing" diff --git a/demonstrations_v2/ml_classical_shadows/metadata.json b/demonstrations_v2/ml_classical_shadows/metadata.json index 19203371f1..dcdea00ba0 100644 --- a/demonstrations_v2/ml_classical_shadows/metadata.json +++ b/demonstrations_v2/ml_classical_shadows/metadata.json @@ -8,7 +8,7 @@ "executable_stable": false, "executable_latest": false, "dateOfPublication": "2022-05-02T00:00:00+00:00", - "dateOfLastModification": "2025-09-09T17:41:50+00:00", + "dateOfLastModification": "2025-09-22T15:48:14+00:00", "categories": [ "Quantum Machine Learning" ], diff --git a/demonstrations_v2/oqc_pulse/metadata.json b/demonstrations_v2/oqc_pulse/metadata.json index 67abb702d2..451cc41b50 100644 --- a/demonstrations_v2/oqc_pulse/metadata.json +++ b/demonstrations_v2/oqc_pulse/metadata.json @@ -8,7 +8,7 @@ "executable_stable": false, "executable_latest": false, "dateOfPublication": "2023-10-30T00:00:00+00:00", - "dateOfLastModification": "2025-09-09T17:41:50+00:00", + "dateOfLastModification": "2025-09-22T15:48:14+00:00", "categories": [ "Quantum Computing" ], diff --git a/demonstrations_v2/plugins_hybrid/demo.py b/demonstrations_v2/plugins_hybrid/demo.py index c4a2d692fc..39a36c3f8d 100644 --- a/demonstrations_v2/plugins_hybrid/demo.py +++ b/demonstrations_v2/plugins_hybrid/demo.py @@ -19,8 +19,8 @@ plugins. We first introduce PennyLane's `Strawberry Fields plugin `_ and use it to explore a non-Gaussian photonic circuit. We then combine this photonic circuit with a qubit circuit — along with some classical processing — to create and optimize a fully hybrid computation. -Be sure to read through the introductory :ref:`qubit rotation ` and -:ref:`Gaussian transformation ` tutorials before attempting this tutorial. +Be sure to read through the introductory :doc:`qubit rotation ` and +:doc:`Gaussian transformation ` tutorials before attempting this tutorial. .. warning:: This demo is only compatible with PennyLane version ``0.29`` or below. @@ -42,7 +42,7 @@ ---------------------- We first consider a photonic circuit which is similar in spirit to the -:ref:`qubit rotation ` circuit: +:doc:`qubit rotation ` circuit: .. figure:: ../_static/demonstration_assets/plugins_hybrid/photon_redirection.png :align: center @@ -294,7 +294,7 @@ def cost(params): # ------------------ # # To really highlight the capabilities of PennyLane, let's now combine the qubit-rotation QNode -# from the :ref:`qubit rotation tutorial ` with the CV photon-redirection +# from the :doc:`qubit rotation tutorial ` with the CV photon-redirection # QNode from above, as well as some classical processing, to produce a truly hybrid # computational model. # diff --git a/demonstrations_v2/plugins_hybrid/metadata.json b/demonstrations_v2/plugins_hybrid/metadata.json index bae7f30d6a..81bcb6db0a 100644 --- a/demonstrations_v2/plugins_hybrid/metadata.json +++ b/demonstrations_v2/plugins_hybrid/metadata.json @@ -8,7 +8,7 @@ "executable_stable": false, "executable_latest": false, "dateOfPublication": "2019-10-11T00:00:00+00:00", - "dateOfLastModification": "2025-09-09T17:41:50+00:00", + "dateOfLastModification": "2025-09-22T15:48:14+00:00", "categories": [ "Devices and Performance" ], diff --git a/demonstrations_v2/pytorch_noise/demo.py b/demonstrations_v2/pytorch_noise/demo.py index 94b572945c..4250ea9dfa 100644 --- a/demonstrations_v2/pytorch_noise/demo.py +++ b/demonstrations_v2/pytorch_noise/demo.py @@ -17,7 +17,7 @@ This demo is only compatible with PennyLane v0.40 or below and Braket v1.31.0. To use Rigetti hardware with newer versions of PennyLane please use the `PennyLane-Braket plugin `__ instead. -Let's revisit the original :ref:`qubit rotation ` tutorial, but instead of +Let's revisit the original :doc:`qubit rotation ` tutorial, but instead of using the default NumPy/autograd QNode interface, we'll use the :doc:`introduction/interfaces/torch`. We'll also replace the ``default.qubit`` device with a noisy ``rigetti.qvm`` device, to see how the optimization responds to noisy qubits. At the end of the @@ -197,7 +197,7 @@ def cost(phi, theta, step): # Note that to run the following script, you will need access to Rigetti's QPU. # To connect to a QPU, we can use Amazon Braket. For a dedicated demonstration # on using Amazon Braket, see our tutorial on -# `Computing gradients in parallel with Amazon Braket `_. +# :doc:`Computing gradients in parallel with Amazon Braket `. import pennylane as qml import torch diff --git a/demonstrations_v2/pytorch_noise/metadata.json b/demonstrations_v2/pytorch_noise/metadata.json index 179e20f210..f183f8b47a 100644 --- a/demonstrations_v2/pytorch_noise/metadata.json +++ b/demonstrations_v2/pytorch_noise/metadata.json @@ -8,7 +8,7 @@ "executable_stable": false, "executable_latest": false, "dateOfPublication": "2019-10-11T00:00:00+00:00", - "dateOfLastModification": "2025-09-09T17:41:50+00:00", + "dateOfLastModification": "2025-09-22T15:48:14+00:00", "categories": [ "Devices and Performance" ], diff --git a/demonstrations_v2/qnn_module_tf/metadata.json b/demonstrations_v2/qnn_module_tf/metadata.json index b7f63cbcf8..bc01bf098e 100644 --- a/demonstrations_v2/qnn_module_tf/metadata.json +++ b/demonstrations_v2/qnn_module_tf/metadata.json @@ -8,7 +8,7 @@ "executable_stable": false, "executable_latest": false, "dateOfPublication": "2020-11-02T00:00:00+00:00", - "dateOfLastModification": "2025-09-09T17:41:50+00:00", + "dateOfLastModification": "2025-09-22T15:48:14+00:00", "categories": [ "Devices and Performance", "Quantum Machine Learning" diff --git a/demonstrations_v2/qnspsa/metadata.json b/demonstrations_v2/qnspsa/metadata.json index 04a39b17b3..956ac53d78 100644 --- a/demonstrations_v2/qnspsa/metadata.json +++ b/demonstrations_v2/qnspsa/metadata.json @@ -8,7 +8,7 @@ "executable_stable": false, "executable_latest": false, "dateOfPublication": "2022-07-18T00:00:00+00:00", - "dateOfLastModification": "2025-09-09T17:41:50+00:00", + "dateOfLastModification": "2025-09-22T15:48:14+00:00", "categories": [ "Optimization" ], diff --git a/demonstrations_v2/qonn/metadata.json b/demonstrations_v2/qonn/metadata.json index 5cce929d27..f536c7b945 100644 --- a/demonstrations_v2/qonn/metadata.json +++ b/demonstrations_v2/qonn/metadata.json @@ -8,7 +8,7 @@ "executable_stable": false, "executable_latest": false, "dateOfPublication": "2020-08-05T00:00:00+00:00", - "dateOfLastModification": "2025-09-09T17:41:50+00:00", + "dateOfLastModification": "2025-09-22T15:48:14+00:00", "categories": [ "Optimization" ], diff --git a/demonstrations_v2/qrack/metadata.json b/demonstrations_v2/qrack/metadata.json index afedd1a25d..adb017cc51 100644 --- a/demonstrations_v2/qrack/metadata.json +++ b/demonstrations_v2/qrack/metadata.json @@ -8,7 +8,7 @@ "executable_stable": false, "executable_latest": false, "dateOfPublication": "2024-07-10T00:00:00+00:00", - "dateOfLastModification": "2025-09-09T17:41:50+00:00", + "dateOfLastModification": "2025-09-22T15:48:14+00:00", "categories": [ "Devices and Performance" ], diff --git a/demonstrations_v2/qsim_beyond_classical/metadata.json b/demonstrations_v2/qsim_beyond_classical/metadata.json index e056dd25e1..e4788ac69c 100644 --- a/demonstrations_v2/qsim_beyond_classical/metadata.json +++ b/demonstrations_v2/qsim_beyond_classical/metadata.json @@ -8,7 +8,7 @@ "executable_stable": false, "executable_latest": false, "dateOfPublication": "2020-11-30T00:00:00+00:00", - "dateOfLastModification": "2025-09-09T17:41:50+00:00", + "dateOfLastModification": "2025-09-22T15:48:14+00:00", "categories": [ "Quantum Hardware", "Quantum Computing" diff --git a/demonstrations_v2/quantum_neural_net/metadata.json b/demonstrations_v2/quantum_neural_net/metadata.json index 1460dd5a78..4c0f7d04bd 100644 --- a/demonstrations_v2/quantum_neural_net/metadata.json +++ b/demonstrations_v2/quantum_neural_net/metadata.json @@ -8,7 +8,7 @@ "executable_stable": false, "executable_latest": false, "dateOfPublication": "2019-10-11T00:00:00+00:00", - "dateOfLastModification": "2025-09-09T17:41:50+00:00", + "dateOfLastModification": "2025-09-22T15:48:14+00:00", "categories": [ "Quantum Machine Learning" ], diff --git a/demonstrations_v2/quantum_volume/metadata.json b/demonstrations_v2/quantum_volume/metadata.json index 9c0a1858c2..09e9d5fff0 100644 --- a/demonstrations_v2/quantum_volume/metadata.json +++ b/demonstrations_v2/quantum_volume/metadata.json @@ -8,7 +8,7 @@ "executable_stable": false, "executable_latest": false, "dateOfPublication": "2020-12-15T00:00:00+00:00", - "dateOfLastModification": "2025-09-09T17:41:50+00:00", + "dateOfLastModification": "2025-09-22T15:48:14+00:00", "categories": [ "Quantum Hardware", "Quantum Computing" diff --git a/demonstrations_v2/tutorial_How_to_optimize_QML_model_using_JAX_and_JAXopt/metadata.json b/demonstrations_v2/tutorial_How_to_optimize_QML_model_using_JAX_and_JAXopt/metadata.json index 2230ee9144..4fef5d309d 100644 --- a/demonstrations_v2/tutorial_How_to_optimize_QML_model_using_JAX_and_JAXopt/metadata.json +++ b/demonstrations_v2/tutorial_How_to_optimize_QML_model_using_JAX_and_JAXopt/metadata.json @@ -11,7 +11,7 @@ "executable_stable": true, "executable_latest": true, "dateOfPublication": "2024-01-18T00:00:00+00:00", - "dateOfLastModification": "2025-09-09T17:41:50+00:00", + "dateOfLastModification": "2025-09-22T15:48:14+00:00", "categories": [ "Quantum Machine Learning", "Optimization", diff --git a/demonstrations_v2/tutorial_How_to_optimize_QML_model_using_JAX_and_Optax/metadata.json b/demonstrations_v2/tutorial_How_to_optimize_QML_model_using_JAX_and_Optax/metadata.json index b7e9f40d33..9d83d33705 100644 --- a/demonstrations_v2/tutorial_How_to_optimize_QML_model_using_JAX_and_Optax/metadata.json +++ b/demonstrations_v2/tutorial_How_to_optimize_QML_model_using_JAX_and_Optax/metadata.json @@ -11,7 +11,7 @@ "executable_stable": true, "executable_latest": true, "dateOfPublication": "2024-01-18T00:00:00+00:00", - "dateOfLastModification": "2025-09-09T17:41:50+00:00", + "dateOfLastModification": "2025-09-22T15:48:14+00:00", "categories": [ "Quantum Machine Learning", "Optimization", diff --git a/demonstrations_v2/tutorial_How_to_optimize_QML_model_using_JAX_catalyst_and_Optax/metadata.json b/demonstrations_v2/tutorial_How_to_optimize_QML_model_using_JAX_catalyst_and_Optax/metadata.json index 9778bdde75..f86b91d488 100644 --- a/demonstrations_v2/tutorial_How_to_optimize_QML_model_using_JAX_catalyst_and_Optax/metadata.json +++ b/demonstrations_v2/tutorial_How_to_optimize_QML_model_using_JAX_catalyst_and_Optax/metadata.json @@ -8,7 +8,7 @@ "executable_stable": true, "executable_latest": true, "dateOfPublication": "2024-04-26T00:00:00+00:00", - "dateOfLastModification": "2025-09-09T17:41:50+00:00", + "dateOfLastModification": "2025-09-22T15:48:14+00:00", "categories": [ "Quantum Machine Learning", "Optimization", 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 b5df1f4175..adccad5ea4 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 @@ -8,7 +8,7 @@ "executable_stable": true, "executable_latest": true, "dateOfPublication": "2024-07-09T00:00:00+00:00", - "dateOfLastModification": "2025-09-09T17:41:50+00:00", + "dateOfLastModification": "2025-09-22T15:48:14+00:00", "categories": [ "Getting Started", "Quantum Computing", diff --git a/demonstrations_v2/tutorial_QGAN/metadata.json b/demonstrations_v2/tutorial_QGAN/metadata.json index c8f83b75c3..77a2f9ed3a 100644 --- a/demonstrations_v2/tutorial_QGAN/metadata.json +++ b/demonstrations_v2/tutorial_QGAN/metadata.json @@ -8,7 +8,7 @@ "executable_stable": true, "executable_latest": true, "dateOfPublication": "2019-10-11T00:00:00+00:00", - "dateOfLastModification": "2025-09-09T17:41:50+00:00", + "dateOfLastModification": "2025-09-22T15:48:14+00:00", "categories": [ "Quantum Machine Learning" ], diff --git a/demonstrations_v2/tutorial_QUBO/metadata.json b/demonstrations_v2/tutorial_QUBO/metadata.json index acd71e9f66..2b1d5c95c8 100644 --- a/demonstrations_v2/tutorial_QUBO/metadata.json +++ b/demonstrations_v2/tutorial_QUBO/metadata.json @@ -8,7 +8,7 @@ "executable_stable": true, "executable_latest": true, "dateOfPublication": "2024-02-29T00:00:00+00:00", - "dateOfLastModification": "2025-09-09T17:41:50+00:00", + "dateOfLastModification": "2025-09-22T15:48:14+00:00", "categories": [ "Optimization" ], diff --git a/demonstrations_v2/tutorial_adaptive_circuits/metadata.json b/demonstrations_v2/tutorial_adaptive_circuits/metadata.json index 8a1a04ddcb..ad4e108fc9 100644 --- a/demonstrations_v2/tutorial_adaptive_circuits/metadata.json +++ b/demonstrations_v2/tutorial_adaptive_circuits/metadata.json @@ -8,7 +8,7 @@ "executable_stable": true, "executable_latest": true, "dateOfPublication": "2021-09-13T00:00:00+00:00", - "dateOfLastModification": "2025-09-09T17:41:50+00:00", + "dateOfLastModification": "2025-09-22T15:48:14+00:00", "categories": [ "Quantum Chemistry" ], @@ -61,7 +61,7 @@ "authors": "Juan Miguel Arrazola", "year": "2021", "journal": "", - "url": "https://pennylane.ai/qml/demos/tutorial_givens_rotations.html" + "url": "https://pennylane.ai/qml/demos/tutorial_givens_rotations" }, { "id": "grimsley2019", diff --git a/demonstrations_v2/tutorial_adjoint_diff/metadata.json b/demonstrations_v2/tutorial_adjoint_diff/metadata.json index a541f36cc2..93c4194c2e 100644 --- a/demonstrations_v2/tutorial_adjoint_diff/metadata.json +++ b/demonstrations_v2/tutorial_adjoint_diff/metadata.json @@ -8,7 +8,7 @@ "executable_stable": true, "executable_latest": true, "dateOfPublication": "2021-11-23T00:00:00+00:00", - "dateOfLastModification": "2025-09-09T17:41:50+00:00", + "dateOfLastModification": "2025-09-22T15:48:14+00:00", "categories": [ "Getting Started" ], diff --git a/demonstrations_v2/tutorial_adversarial_attacks_QML/metadata.json b/demonstrations_v2/tutorial_adversarial_attacks_QML/metadata.json index e9060dea6f..91a65dbfbc 100644 --- a/demonstrations_v2/tutorial_adversarial_attacks_QML/metadata.json +++ b/demonstrations_v2/tutorial_adversarial_attacks_QML/metadata.json @@ -11,7 +11,7 @@ "executable_stable": true, "executable_latest": true, "dateOfPublication": "2024-09-16T00:00:00+00:00", - "dateOfLastModification": "2025-09-09T17:41:50+00:00", + "dateOfLastModification": "2025-09-22T15:48:14+00:00", "categories": [ "Quantum Machine Learning", "Quantum Computing" diff --git a/demonstrations_v2/tutorial_annni/metadata.json b/demonstrations_v2/tutorial_annni/metadata.json index 8777c7d2bd..0388b43394 100644 --- a/demonstrations_v2/tutorial_annni/metadata.json +++ b/demonstrations_v2/tutorial_annni/metadata.json @@ -8,7 +8,7 @@ "executable_stable": true, "executable_latest": true, "dateOfPublication": "2025-04-28T00:00:00+00:00", - "dateOfLastModification": "2025-09-09T17:41:50+00:00", + "dateOfLastModification": "2025-09-22T15:48:14+00:00", "categories": [ "Quantum Machine Learning" ], diff --git a/demonstrations_v2/tutorial_apply_qsvt/metadata.json b/demonstrations_v2/tutorial_apply_qsvt/metadata.json index a148268a13..d51c5293ca 100644 --- a/demonstrations_v2/tutorial_apply_qsvt/metadata.json +++ b/demonstrations_v2/tutorial_apply_qsvt/metadata.json @@ -11,7 +11,7 @@ "executable_stable": true, "executable_latest": true, "dateOfPublication": "2023-08-22T00:00:00+00:00", - "dateOfLastModification": "2025-09-09T17:41:50+00:00", + "dateOfLastModification": "2025-09-22T15:48:14+00:00", "categories": [ "Quantum Computing", "Algorithms", diff --git a/demonstrations_v2/tutorial_backprop/metadata.json b/demonstrations_v2/tutorial_backprop/metadata.json index 03d64162ca..44d1de3693 100644 --- a/demonstrations_v2/tutorial_backprop/metadata.json +++ b/demonstrations_v2/tutorial_backprop/metadata.json @@ -8,7 +8,7 @@ "executable_stable": true, "executable_latest": true, "dateOfPublication": "2020-08-11T00:00:00+00:00", - "dateOfLastModification": "2025-09-09T17:41:50+00:00", + "dateOfLastModification": "2025-09-22T15:48:14+00:00", "categories": [ "Getting Started" ], diff --git a/demonstrations_v2/tutorial_barren_gadgets/metadata.json b/demonstrations_v2/tutorial_barren_gadgets/metadata.json index d4560bc6aa..a129ac14dd 100644 --- a/demonstrations_v2/tutorial_barren_gadgets/metadata.json +++ b/demonstrations_v2/tutorial_barren_gadgets/metadata.json @@ -8,7 +8,7 @@ "executable_stable": true, "executable_latest": true, "dateOfPublication": "2022-12-09T00:00:00+00:00", - "dateOfLastModification": "2025-09-09T17:41:50+00:00", + "dateOfLastModification": "2025-09-22T15:48:14+00:00", "categories": [ "Optimization" ], diff --git a/demonstrations_v2/tutorial_barren_plateaus/metadata.json b/demonstrations_v2/tutorial_barren_plateaus/metadata.json index 5e6b3df9e6..b409e9d56e 100644 --- a/demonstrations_v2/tutorial_barren_plateaus/metadata.json +++ b/demonstrations_v2/tutorial_barren_plateaus/metadata.json @@ -8,7 +8,7 @@ "executable_stable": true, "executable_latest": true, "dateOfPublication": "2019-10-11T00:00:00+00:00", - "dateOfLastModification": "2025-09-09T17:41:50+00:00", + "dateOfLastModification": "2025-09-22T15:48:14+00:00", "categories": [ "Optimization" ], diff --git a/demonstrations_v2/tutorial_block_encoding/metadata.json b/demonstrations_v2/tutorial_block_encoding/metadata.json index 0b903836ba..9f5bc066cc 100644 --- a/demonstrations_v2/tutorial_block_encoding/metadata.json +++ b/demonstrations_v2/tutorial_block_encoding/metadata.json @@ -14,7 +14,7 @@ "executable_stable": true, "executable_latest": true, "dateOfPublication": "2023-11-28T00:00:00+00:00", - "dateOfLastModification": "2025-09-09T17:41:50+00:00", + "dateOfLastModification": "2025-09-22T15:48:14+00:00", "categories": [ "Quantum Computing", "Algorithms" diff --git a/demonstrations_v2/tutorial_bluequbit/metadata.json b/demonstrations_v2/tutorial_bluequbit/metadata.json index 6c4d3b06fd..999d2ba79a 100644 --- a/demonstrations_v2/tutorial_bluequbit/metadata.json +++ b/demonstrations_v2/tutorial_bluequbit/metadata.json @@ -8,7 +8,7 @@ "executable_stable": true, "executable_latest": true, "dateOfPublication": "2024-09-24T00:00:00+00:00", - "dateOfLastModification": "2025-09-09T17:41:50+00:00", + "dateOfLastModification": "2025-09-22T15:48:14+00:00", "categories": [ "Devices and Performance", "Quantum Computing" diff --git a/demonstrations_v2/tutorial_bp_catalyst/metadata.json b/demonstrations_v2/tutorial_bp_catalyst/metadata.json index e3e634bfbd..392bd6a637 100644 --- a/demonstrations_v2/tutorial_bp_catalyst/metadata.json +++ b/demonstrations_v2/tutorial_bp_catalyst/metadata.json @@ -6,7 +6,7 @@ } ], "dateOfPublication": "2025-08-25T14:30:00+00:00", - "dateOfLastModification": "2025-09-09T17:41:50+00:00", + "dateOfLastModification": "2025-09-22T15:48:14+00:00", "categories": [ "Quantum Computing" ], diff --git a/demonstrations_v2/tutorial_chemical_reactions/metadata.json b/demonstrations_v2/tutorial_chemical_reactions/metadata.json index f6dedeb66e..a1c3ef44d6 100644 --- a/demonstrations_v2/tutorial_chemical_reactions/metadata.json +++ b/demonstrations_v2/tutorial_chemical_reactions/metadata.json @@ -11,7 +11,7 @@ "executable_stable": true, "executable_latest": true, "dateOfPublication": "2021-07-23T00:00:00+00:00", - "dateOfLastModification": "2025-09-09T17:41:50+00:00", + "dateOfLastModification": "2025-09-22T15:48:14+00:00", "categories": [ "Quantum Chemistry" ], diff --git a/demonstrations_v2/tutorial_circuit_compilation/metadata.json b/demonstrations_v2/tutorial_circuit_compilation/metadata.json index 75899322b0..c5b0cc7c35 100644 --- a/demonstrations_v2/tutorial_circuit_compilation/metadata.json +++ b/demonstrations_v2/tutorial_circuit_compilation/metadata.json @@ -8,7 +8,7 @@ "executable_stable": true, "executable_latest": true, "dateOfPublication": "2023-06-14T00:00:00+00:00", - "dateOfLastModification": "2025-09-09T17:41:50+00:00", + "dateOfLastModification": "2025-09-22T15:48:14+00:00", "categories": [ "Getting Started", "Devices and Performance" diff --git a/demonstrations_v2/tutorial_classical_expval_estimation/metadata.json b/demonstrations_v2/tutorial_classical_expval_estimation/metadata.json index 0a03b6c3e6..94a6b9fb0b 100644 --- a/demonstrations_v2/tutorial_classical_expval_estimation/metadata.json +++ b/demonstrations_v2/tutorial_classical_expval_estimation/metadata.json @@ -8,7 +8,7 @@ "executable_stable": true, "executable_latest": true, "dateOfPublication": "2024-09-10T00:00:00+00:00", - "dateOfLastModification": "2025-09-09T17:41:50+00:00", + "dateOfLastModification": "2025-09-22T15:48:14+00:00", "categories": [ "Quantum Computing" ], diff --git a/demonstrations_v2/tutorial_classical_kernels/demo.py b/demonstrations_v2/tutorial_classical_kernels/demo.py index 2fd4e11460..8ad9603450 100644 --- a/demonstrations_v2/tutorial_classical_kernels/demo.py +++ b/demonstrations_v2/tutorial_classical_kernels/demo.py @@ -43,7 +43,7 @@ Two functions can only have the same Fourier spectrum if they are the same function. It turns out that, for certain classes of quantum circuits, `we can theoretically describe the Fourier spectrum rather well -`_. +:doc:`demos/tutorial_expressivity_fourier_series`. Using this theory, together with some good old-fashioned convex optimization, we will derive a quantum circuit that approximates the famous Gaussian kernel. @@ -78,8 +78,8 @@ PennyLane), check out the following demos, which cover different aspects extensively: -#. `Training and evaluating quantum kernels `_ -#. `Kernel-based training of quantum models with scikit-learn `_ +#. :doc:`Training and evaluating quantum kernels ` +#. :doc:`Kernel-based training of quantum models with scikit-learn ` For the purposes of this demo, a *kernel* is a real-valued function of two variables :math:`k(x_1,x_2)` from a given data domain :math:`x_1, @@ -313,7 +313,7 @@ def fourier_p(d): # The quantum kernel considered in this demo. # # We construct the quantum kernel from a quantum embedding (see the demo on -# `Quantum Embedding Kernels `_). +# :doc:`Quantum Embedding Kernels `). # The quantum embedding circuit will consist of two parts. # The first one, trainable, will be a parametrized general state preparation # scheme :math:`W_a,` with parameters :math:`a.` diff --git a/demonstrations_v2/tutorial_classical_kernels/metadata.json b/demonstrations_v2/tutorial_classical_kernels/metadata.json index 47b4aac171..c687f5be8f 100644 --- a/demonstrations_v2/tutorial_classical_kernels/metadata.json +++ b/demonstrations_v2/tutorial_classical_kernels/metadata.json @@ -8,7 +8,7 @@ "executable_stable": true, "executable_latest": true, "dateOfPublication": "2022-03-01T00:00:00+00:00", - "dateOfLastModification": "2025-09-09T17:41:50+00:00", + "dateOfLastModification": "2025-09-22T15:48:14+00:00", "categories": [ "Quantum Machine Learning" ], diff --git a/demonstrations_v2/tutorial_classical_shadows/metadata.json b/demonstrations_v2/tutorial_classical_shadows/metadata.json index 5f02a8d41e..3eb801a1ab 100644 --- a/demonstrations_v2/tutorial_classical_shadows/metadata.json +++ b/demonstrations_v2/tutorial_classical_shadows/metadata.json @@ -11,7 +11,7 @@ "executable_stable": true, "executable_latest": true, "dateOfPublication": "2021-06-14T00:00:00+00:00", - "dateOfLastModification": "2025-09-09T17:41:50+00:00", + "dateOfLastModification": "2025-09-22T15:48:14+00:00", "categories": [ "Algorithms" ], diff --git a/demonstrations_v2/tutorial_classically_boosted_vqe/metadata.json b/demonstrations_v2/tutorial_classically_boosted_vqe/metadata.json index bcb18a1d57..c04d722658 100644 --- a/demonstrations_v2/tutorial_classically_boosted_vqe/metadata.json +++ b/demonstrations_v2/tutorial_classically_boosted_vqe/metadata.json @@ -11,7 +11,7 @@ "executable_stable": true, "executable_latest": true, "dateOfPublication": "2022-10-31T00:00:00+00:00", - "dateOfLastModification": "2025-09-09T17:41:50+00:00", + "dateOfLastModification": "2025-09-22T15:48:14+00:00", "categories": [ "Quantum Chemistry" ], diff --git a/demonstrations_v2/tutorial_clifford_circuit_simulations/metadata.json b/demonstrations_v2/tutorial_clifford_circuit_simulations/metadata.json index c812ec36ef..f502541b7f 100644 --- a/demonstrations_v2/tutorial_clifford_circuit_simulations/metadata.json +++ b/demonstrations_v2/tutorial_clifford_circuit_simulations/metadata.json @@ -8,7 +8,7 @@ "executable_stable": true, "executable_latest": true, "dateOfPublication": "2024-04-12T00:00:00+00:00", - "dateOfLastModification": "2025-09-16T17:41:50+00:00", + "dateOfLastModification": "2025-09-22T15:48:14+00:00", "categories": [ "Devices and Performance" ], diff --git a/demonstrations_v2/tutorial_coherent_vqls/metadata.json b/demonstrations_v2/tutorial_coherent_vqls/metadata.json index c6d2f36c24..54057d17ab 100644 --- a/demonstrations_v2/tutorial_coherent_vqls/metadata.json +++ b/demonstrations_v2/tutorial_coherent_vqls/metadata.json @@ -8,7 +8,7 @@ "executable_stable": true, "executable_latest": true, "dateOfPublication": "2019-11-06T00:00:00+00:00", - "dateOfLastModification": "2025-09-09T17:41:50+00:00", + "dateOfLastModification": "2025-09-22T15:48:14+00:00", "categories": [ "Optimization" ], diff --git a/demonstrations_v2/tutorial_constant_depth_mps_prep/metadata.json b/demonstrations_v2/tutorial_constant_depth_mps_prep/metadata.json index 826d2e40ad..96cffe0ff4 100644 --- a/demonstrations_v2/tutorial_constant_depth_mps_prep/metadata.json +++ b/demonstrations_v2/tutorial_constant_depth_mps_prep/metadata.json @@ -8,7 +8,7 @@ "executable_stable": true, "executable_latest": true, "dateOfPublication": "2024-10-09T00:00:00+00:00", - "dateOfLastModification": "2025-09-09T17:41:50+00:00", + "dateOfLastModification": "2025-09-22T15:48:14+00:00", "categories": [ "Quantum Computing", "Algorithms" diff --git a/demonstrations_v2/tutorial_contextuality/demo.py b/demonstrations_v2/tutorial_contextuality/demo.py index 97745e4571..4771bf6e24 100644 --- a/demonstrations_v2/tutorial_contextuality/demo.py +++ b/demonstrations_v2/tutorial_contextuality/demo.py @@ -435,7 +435,7 @@ def input_prep(alpha): # # This kind of reasoning is an example of geometric quantum machine # learning (check out [#reptheory]_ and [#equivariant]_ or our own -# `demo `__ for an awesome introduction to the subject). +# :doc:`demo ` for an awesome introduction to the subject). # Below we construct the # bias invariant layer: note that all the generators commute with # :math:`Z_0+Z_1+Z_2.` The variables ``blocks`` and ``layers`` are model diff --git a/demonstrations_v2/tutorial_contextuality/metadata.json b/demonstrations_v2/tutorial_contextuality/metadata.json index 66a017abe2..87e3d9744b 100644 --- a/demonstrations_v2/tutorial_contextuality/metadata.json +++ b/demonstrations_v2/tutorial_contextuality/metadata.json @@ -8,7 +8,7 @@ "executable_stable": true, "executable_latest": true, "dateOfPublication": "2023-09-06T00:00:00+00:00", - "dateOfLastModification": "2025-09-09T17:41:50+00:00", + "dateOfLastModification": "2025-09-22T15:48:14+00:00", "categories": [ "Quantum Machine Learning" ], diff --git a/demonstrations_v2/tutorial_data_reuploading_classifier/metadata.json b/demonstrations_v2/tutorial_data_reuploading_classifier/metadata.json index 9dd3a17228..2fca1ef7e1 100644 --- a/demonstrations_v2/tutorial_data_reuploading_classifier/metadata.json +++ b/demonstrations_v2/tutorial_data_reuploading_classifier/metadata.json @@ -8,7 +8,7 @@ "executable_stable": true, "executable_latest": true, "dateOfPublication": "2019-10-11T00:00:00+00:00", - "dateOfLastModification": "2025-09-09T17:41:50+00:00", + "dateOfLastModification": "2025-09-22T15:48:14+00:00", "categories": [ "Quantum Machine Learning" ], diff --git a/demonstrations_v2/tutorial_diffable-mitigation/metadata.json b/demonstrations_v2/tutorial_diffable-mitigation/metadata.json index 427ff31ccb..6df854bc43 100644 --- a/demonstrations_v2/tutorial_diffable-mitigation/metadata.json +++ b/demonstrations_v2/tutorial_diffable-mitigation/metadata.json @@ -8,7 +8,7 @@ "executable_stable": true, "executable_latest": true, "dateOfPublication": "2022-08-22T00:00:00+00:00", - "dateOfLastModification": "2025-09-09T17:41:50+00:00", + "dateOfLastModification": "2025-09-22T15:48:14+00:00", "categories": [ "Optimization" ], diff --git a/demonstrations_v2/tutorial_diffable_shadows/metadata.json b/demonstrations_v2/tutorial_diffable_shadows/metadata.json index 0360fb4a85..94def1570b 100644 --- a/demonstrations_v2/tutorial_diffable_shadows/metadata.json +++ b/demonstrations_v2/tutorial_diffable_shadows/metadata.json @@ -8,7 +8,7 @@ "executable_stable": true, "executable_latest": true, "dateOfPublication": "2022-10-07T00:00:00+00:00", - "dateOfLastModification": "2025-09-09T17:41:50+00:00", + "dateOfLastModification": "2025-09-22T15:48:14+00:00", "categories": [ "Algorithms", "Quantum Computing" diff --git a/demonstrations_v2/tutorial_differentiable_HF/demo.py b/demonstrations_v2/tutorial_differentiable_HF/demo.py index fd2f023ecf..0d89ef58b5 100644 --- a/demonstrations_v2/tutorial_differentiable_HF/demo.py +++ b/demonstrations_v2/tutorial_differentiable_HF/demo.py @@ -21,7 +21,7 @@ equations to obtain optimized orbitals which can be used to construct fully-differentiable molecular Hamiltonians. PennyLane allows users to natively compute derivatives of all these objects with respect to the underlying parameters using the methods of -`automatic differentiation `_. We +`automatic differentiation `_. We introduce a workflow to jointly optimize circuit parameters, nuclear coordinates and basis set parameters in a variational quantum eigensolver algorithm. You will also learn how to visualize the atomic and molecular orbitals which can be used to create an animation like this: @@ -64,7 +64,7 @@ Efficient optimization of the molecular Hamiltonian parameters in a variational quantum algorithm is essential for tackling problems such as -`geometry optimization `_ and vibrational +:doc:`geometry optimization ` and vibrational frequency calculations. These problems require computing the first- and second-order derivatives of the molecular energy with respect to nuclear coordinates which can be efficiently obtained if the diff --git a/demonstrations_v2/tutorial_differentiable_HF/metadata.json b/demonstrations_v2/tutorial_differentiable_HF/metadata.json index 55d9a442ba..471c04820f 100644 --- a/demonstrations_v2/tutorial_differentiable_HF/metadata.json +++ b/demonstrations_v2/tutorial_differentiable_HF/metadata.json @@ -8,7 +8,7 @@ "executable_stable": true, "executable_latest": true, "dateOfPublication": "2022-05-09T00:00:00+00:00", - "dateOfLastModification": "2025-09-09T17:41:50+00:00", + "dateOfLastModification": "2025-09-22T15:48:14+00:00", "categories": [ "Quantum Chemistry" ], diff --git a/demonstrations_v2/tutorial_doubly_stochastic/metadata.json b/demonstrations_v2/tutorial_doubly_stochastic/metadata.json index 628d06b969..f228b1fe89 100644 --- a/demonstrations_v2/tutorial_doubly_stochastic/metadata.json +++ b/demonstrations_v2/tutorial_doubly_stochastic/metadata.json @@ -8,7 +8,7 @@ "executable_stable": true, "executable_latest": true, "dateOfPublication": "2019-10-16T00:00:00+00:00", - "dateOfLastModification": "2025-09-09T17:41:50+00:00", + "dateOfLastModification": "2025-09-22T15:48:14+00:00", "categories": [ "Optimization" ], diff --git a/demonstrations_v2/tutorial_dqi/metadata.json b/demonstrations_v2/tutorial_dqi/metadata.json index fe7612c8ab..55e980e095 100644 --- a/demonstrations_v2/tutorial_dqi/metadata.json +++ b/demonstrations_v2/tutorial_dqi/metadata.json @@ -8,7 +8,7 @@ "executable_stable": true, "executable_latest": true, "dateOfPublication": "2025-09-19T10:00:00+00:00", - "dateOfLastModification": "2025-09-19T10:00:00+00:01", + "dateOfLastModification": "2025-09-22T15:48:14+00:00", "categories": [ "Algorithms" ], diff --git a/demonstrations_v2/tutorial_eqnn_force_field/metadata.json b/demonstrations_v2/tutorial_eqnn_force_field/metadata.json index 64f4e68d18..0d932f545a 100644 --- a/demonstrations_v2/tutorial_eqnn_force_field/metadata.json +++ b/demonstrations_v2/tutorial_eqnn_force_field/metadata.json @@ -11,7 +11,7 @@ "executable_stable": true, "executable_latest": true, "dateOfPublication": "2024-03-12T00:00:00+00:00", - "dateOfLastModification": "2025-09-09T17:41:50+00:00", + "dateOfLastModification": "2025-09-22T15:48:14+00:00", "categories": [ "Quantum Machine Learning", "Quantum Chemistry" diff --git a/demonstrations_v2/tutorial_equivariant_graph_embedding/demo.py b/demonstrations_v2/tutorial_equivariant_graph_embedding/demo.py index 3dd94f1289..b013b83540 100644 --- a/demonstrations_v2/tutorial_equivariant_graph_embedding/demo.py +++ b/demonstrations_v2/tutorial_equivariant_graph_embedding/demo.py @@ -46,7 +46,7 @@ # .. note:: # The tutorial is meant for beginners and does not contain the mathematical details of the # rich theory of equivariance. Have a look -# `at this demo `_ if you want to know more. +# :doc:`at this demo ` if you want to know more. # # # Permuted adjacency matrices describe the same graph @@ -148,7 +148,7 @@ def permute(A, permutation): # # When we design a machine learning model that takes graph data, the first step is to encode # the adjacency matrix into a quantum state using an embedding or -# `quantum feature map `_ +# `quantum feature map `_ # :math:`\phi:` # # .. math:: diff --git a/demonstrations_v2/tutorial_equivariant_graph_embedding/metadata.json b/demonstrations_v2/tutorial_equivariant_graph_embedding/metadata.json index d1a393cce9..71bdd65890 100644 --- a/demonstrations_v2/tutorial_equivariant_graph_embedding/metadata.json +++ b/demonstrations_v2/tutorial_equivariant_graph_embedding/metadata.json @@ -8,7 +8,7 @@ "executable_stable": true, "executable_latest": true, "dateOfPublication": "2023-07-13T00:00:00+00:00", - "dateOfLastModification": "2025-09-09T17:41:50+00:00", + "dateOfLastModification": "2025-09-22T15:48:14+00:00", "categories": [ "Quantum Machine Learning" ], diff --git a/demonstrations_v2/tutorial_error_mitigation/metadata.json b/demonstrations_v2/tutorial_error_mitigation/metadata.json index 16c32cad91..98c138324e 100644 --- a/demonstrations_v2/tutorial_error_mitigation/metadata.json +++ b/demonstrations_v2/tutorial_error_mitigation/metadata.json @@ -11,7 +11,7 @@ "executable_stable": true, "executable_latest": false, "dateOfPublication": "2021-11-29T00:00:00+00:00", - "dateOfLastModification": "2025-09-09T17:41:50+00:00", + "dateOfLastModification": "2025-09-22T15:48:14+00:00", "categories": [ "Algorithms", "Quantum Computing" diff --git a/demonstrations_v2/tutorial_error_prop/metadata.json b/demonstrations_v2/tutorial_error_prop/metadata.json index a1710574ad..4fa177f9de 100644 --- a/demonstrations_v2/tutorial_error_prop/metadata.json +++ b/demonstrations_v2/tutorial_error_prop/metadata.json @@ -8,7 +8,7 @@ "executable_stable": true, "executable_latest": true, "dateOfPublication": "2024-05-03T00:00:00+00:00", - "dateOfLastModification": "2025-09-09T17:41:50+00:00", + "dateOfLastModification": "2025-09-22T15:48:14+00:00", "categories": [ "Algorithms", "Quantum Computing", diff --git a/demonstrations_v2/tutorial_expressivity_fourier_series/metadata.json b/demonstrations_v2/tutorial_expressivity_fourier_series/metadata.json index d289484bcd..00658e5cb1 100644 --- a/demonstrations_v2/tutorial_expressivity_fourier_series/metadata.json +++ b/demonstrations_v2/tutorial_expressivity_fourier_series/metadata.json @@ -11,7 +11,7 @@ "executable_stable": true, "executable_latest": true, "dateOfPublication": "2020-08-24T00:00:00+00:00", - "dateOfLastModification": "2025-09-09T17:41:50+00:00", + "dateOfLastModification": "2025-09-22T15:48:14+00:00", "categories": [ "Quantum Machine Learning" ], diff --git a/demonstrations_v2/tutorial_falqon/demo.py b/demonstrations_v2/tutorial_falqon/demo.py index 5f429e3e51..0fa9993723 100644 --- a/demonstrations_v2/tutorial_falqon/demo.py +++ b/demonstrations_v2/tutorial_falqon/demo.py @@ -15,7 +15,7 @@ ----------------------------- While the -`Quantum Approximate Optimization Algorithm (QAOA) `__ +:doc:`Quantum Approximate Optimization Algorithm (QAOA) ` is one of the best-known processes for solving combinatorial optimization problems with quantum computers, it has a major drawback: convergence isn't guaranteed, as the optimization procedure can become "stuck" in local minima. @@ -32,7 +32,7 @@ .. note:: If you are not familiar with QAOA, we recommend checking out the - `Intro to QAOA tutorial `__, + `Intro to QAOA tutorial `__, since many of the same ideas carry over and will be used throughout this demonstration. Theory diff --git a/demonstrations_v2/tutorial_falqon/metadata.json b/demonstrations_v2/tutorial_falqon/metadata.json index a03139af91..c4f933b023 100644 --- a/demonstrations_v2/tutorial_falqon/metadata.json +++ b/demonstrations_v2/tutorial_falqon/metadata.json @@ -11,7 +11,7 @@ "executable_stable": true, "executable_latest": true, "dateOfPublication": "2021-05-21T00:00:00+00:00", - "dateOfLastModification": "2025-09-09T17:41:50+00:00", + "dateOfLastModification": "2025-09-22T15:48:14+00:00", "categories": [ "Optimization" ], diff --git a/demonstrations_v2/tutorial_fermionic_operators/metadata.json b/demonstrations_v2/tutorial_fermionic_operators/metadata.json index 369decd1ee..f83719b73d 100644 --- a/demonstrations_v2/tutorial_fermionic_operators/metadata.json +++ b/demonstrations_v2/tutorial_fermionic_operators/metadata.json @@ -8,7 +8,7 @@ "executable_stable": true, "executable_latest": true, "dateOfPublication": "2023-06-27T00:00:00+00:00", - "dateOfLastModification": "2025-09-09T17:41:50+00:00", + "dateOfLastModification": "2025-09-22T15:48:14+00:00", "categories": [ "Quantum Chemistry" ], diff --git a/demonstrations_v2/tutorial_fixed_depth_hamiltonian_simulation_via_cartan_decomposition/metadata.json b/demonstrations_v2/tutorial_fixed_depth_hamiltonian_simulation_via_cartan_decomposition/metadata.json index 9e2ae6ad61..d47a5584b4 100644 --- a/demonstrations_v2/tutorial_fixed_depth_hamiltonian_simulation_via_cartan_decomposition/metadata.json +++ b/demonstrations_v2/tutorial_fixed_depth_hamiltonian_simulation_via_cartan_decomposition/metadata.json @@ -8,7 +8,7 @@ "executable_stable": true, "executable_latest": true, "dateOfPublication": "2024-12-19T00:00:00+00:00", - "dateOfLastModification": "2025-09-09T17:41:50+00:00", + "dateOfLastModification": "2025-09-22T15:48:14+00:00", "categories": [ "Quantum Computing", "Algorithms" diff --git a/demonstrations_v2/tutorial_game_of_surface_codes/metadata.json b/demonstrations_v2/tutorial_game_of_surface_codes/metadata.json index 7bc9004d95..9b7d0e98b1 100644 --- a/demonstrations_v2/tutorial_game_of_surface_codes/metadata.json +++ b/demonstrations_v2/tutorial_game_of_surface_codes/metadata.json @@ -8,7 +8,7 @@ "executable_stable": true, "executable_latest": true, "dateOfPublication": "2025-06-02T00:00:00+00:00", - "dateOfLastModification": "2025-09-09T17:41:50+00:00", + "dateOfLastModification": "2025-09-22T15:48:14+00:00", "categories": [ "Quantum Computing", "Algorithms" diff --git a/demonstrations_v2/tutorial_gaussian_transformation/demo.py b/demonstrations_v2/tutorial_gaussian_transformation/demo.py index 7a7c34d156..81ea2dff19 100644 --- a/demonstrations_v2/tutorial_gaussian_transformation/demo.py +++ b/demonstrations_v2/tutorial_gaussian_transformation/demo.py @@ -99,7 +99,7 @@ def mean_photon_gaussian(mag_alpha, phase_alpha, phi): # Optimization # ------------ # -# As in the :ref:`qubit rotation ` tutorial, let's now use one +# As in the :doc:`qubit rotation ` tutorial, let's now use one # of the ``jaxopt`` optimizers in order to optimize the quantum circuit # towards the desired output. We want the mean photon number to be exactly one, # so we will use a squared-difference cost function: @@ -159,7 +159,7 @@ def cost(params): # do not change during the optimization. Only the magnitude of the complex # displacement :math:`|\alpha|` affects the mean photon number of the circuit. # -# Continue on to the next tutorial, :ref:`plugins_hybrid`, to learn how to +# Continue on to the next tutorial, :doc:`plugins hybrid `, to learn how to # utilize the extensive plugin ecosystem of PennyLane, # build continuous-variable (CV) quantum nodes, and to see an example of a # hybrid qubit-CV-classical computation using PennyLane. diff --git a/demonstrations_v2/tutorial_gaussian_transformation/metadata.json b/demonstrations_v2/tutorial_gaussian_transformation/metadata.json index b5e5cbe6fb..9651ab4aba 100644 --- a/demonstrations_v2/tutorial_gaussian_transformation/metadata.json +++ b/demonstrations_v2/tutorial_gaussian_transformation/metadata.json @@ -8,7 +8,7 @@ "executable_stable": true, "executable_latest": true, "dateOfPublication": "2019-10-11T00:00:00+00:00", - "dateOfLastModification": "2025-09-09T17:41:50+00:00", + "dateOfLastModification": "2025-09-22T15:48:14+00:00", "categories": [ "Getting Started" ], diff --git a/demonstrations_v2/tutorial_general_parshift/metadata.json b/demonstrations_v2/tutorial_general_parshift/metadata.json index 6ce1891040..d03bd443c7 100644 --- a/demonstrations_v2/tutorial_general_parshift/metadata.json +++ b/demonstrations_v2/tutorial_general_parshift/metadata.json @@ -8,7 +8,7 @@ "executable_stable": true, "executable_latest": true, "dateOfPublication": "2021-08-23T00:00:00+00:00", - "dateOfLastModification": "2025-09-09T17:41:50+00:00", + "dateOfLastModification": "2025-09-22T15:48:14+00:00", "categories": [ "Optimization" ], diff --git a/demonstrations_v2/tutorial_geometric_qml/metadata.json b/demonstrations_v2/tutorial_geometric_qml/metadata.json index f8ba94e8fd..ad4c4a5619 100644 --- a/demonstrations_v2/tutorial_geometric_qml/metadata.json +++ b/demonstrations_v2/tutorial_geometric_qml/metadata.json @@ -8,7 +8,7 @@ "executable_stable": true, "executable_latest": true, "dateOfPublication": "2022-10-18T00:00:00+00:00", - "dateOfLastModification": "2025-09-09T17:41:50+00:00", + "dateOfLastModification": "2025-09-22T15:48:14+00:00", "categories": [ "Quantum Machine Learning" ], diff --git a/demonstrations_v2/tutorial_givens_rotations/metadata.json b/demonstrations_v2/tutorial_givens_rotations/metadata.json index 88d7603ec7..ad01a86958 100644 --- a/demonstrations_v2/tutorial_givens_rotations/metadata.json +++ b/demonstrations_v2/tutorial_givens_rotations/metadata.json @@ -8,7 +8,7 @@ "executable_stable": true, "executable_latest": true, "dateOfPublication": "2021-06-30T00:00:00+00:00", - "dateOfLastModification": "2025-09-09T17:41:50+00:00", + "dateOfLastModification": "2025-09-22T15:48:14+00:00", "categories": [ "Quantum Chemistry" ], diff --git a/demonstrations_v2/tutorial_grovers_algorithm/metadata.json b/demonstrations_v2/tutorial_grovers_algorithm/metadata.json index d99a3fdb84..3b129910cd 100644 --- a/demonstrations_v2/tutorial_grovers_algorithm/metadata.json +++ b/demonstrations_v2/tutorial_grovers_algorithm/metadata.json @@ -8,7 +8,7 @@ "executable_stable": true, "executable_latest": true, "dateOfPublication": "2023-07-03T00:00:00+00:00", - "dateOfLastModification": "2025-09-09T17:41:50+00:00", + "dateOfLastModification": "2025-09-22T15:48:14+00:00", "categories": [ "Getting Started", "Algorithms", diff --git a/demonstrations_v2/tutorial_guide_to_pennylane_knowing_qiskit/metadata.json b/demonstrations_v2/tutorial_guide_to_pennylane_knowing_qiskit/metadata.json index e90e0ba59d..077e08b41a 100644 --- a/demonstrations_v2/tutorial_guide_to_pennylane_knowing_qiskit/metadata.json +++ b/demonstrations_v2/tutorial_guide_to_pennylane_knowing_qiskit/metadata.json @@ -8,7 +8,7 @@ "executable_stable": true, "executable_latest": true, "dateOfPublication": "2024-07-22T00:00:00+00:00", - "dateOfLastModification": "2025-09-09T17:41:50+00:00", + "dateOfLastModification": "2025-09-22T15:48:14+00:00", "categories": [ "Quantum Computing" ], diff --git a/demonstrations_v2/tutorial_haar_measure/metadata.json b/demonstrations_v2/tutorial_haar_measure/metadata.json index 08504ff4df..9b4afc2132 100644 --- a/demonstrations_v2/tutorial_haar_measure/metadata.json +++ b/demonstrations_v2/tutorial_haar_measure/metadata.json @@ -8,7 +8,7 @@ "executable_stable": true, "executable_latest": true, "dateOfPublication": "2021-03-22T00:00:00+00:00", - "dateOfLastModification": "2024-10-11T00:00:00+00:00", + "dateOfLastModification": "2025-09-22T15:48:14+00:00", "categories": [ "Quantum Machine Learning", "Quantum Computing" @@ -143,4 +143,4 @@ } ], "discussionForumUrl": "https://discuss.pennylane.ai/t/understanding-the-haar-measure-demo/7334" -} +} \ No newline at end of file diff --git a/demonstrations_v2/tutorial_here_comes_the_sun/demo.py b/demonstrations_v2/tutorial_here_comes_the_sun/demo.py index 5cdd25a215..63cbe697c1 100644 --- a/demonstrations_v2/tutorial_here_comes_the_sun/demo.py +++ b/demonstrations_v2/tutorial_here_comes_the_sun/demo.py @@ -29,7 +29,7 @@ can act like *any* gate on its qubits by choosing the parameters accordingly. We will look at a custom derivative rule [#wiersema]_ for this gate and compare it to two alternative differentiation strategies, namely finite differences and the `stochastic -parameter-shift rule `_. +parameter-shift rule `_. Finally, we will compare the performance of ``qml.SpecialUnitary`` for a toy minimization problem to that of two other general local gates. That is, we compare the trainability of equally expressive ansätze. diff --git a/demonstrations_v2/tutorial_here_comes_the_sun/metadata.json b/demonstrations_v2/tutorial_here_comes_the_sun/metadata.json index 6518a66b5a..d470175afc 100644 --- a/demonstrations_v2/tutorial_here_comes_the_sun/metadata.json +++ b/demonstrations_v2/tutorial_here_comes_the_sun/metadata.json @@ -8,7 +8,7 @@ "executable_stable": true, "executable_latest": true, "dateOfPublication": "2023-04-03T00:00:00+00:00", - "dateOfLastModification": "2025-09-09T17:41:50+00:00", + "dateOfLastModification": "2025-09-22T15:48:14+00:00", "categories": [ "Optimization" ], diff --git a/demonstrations_v2/tutorial_hidden_cut/metadata.json b/demonstrations_v2/tutorial_hidden_cut/metadata.json index 0ad4440582..ec08e32cae 100644 --- a/demonstrations_v2/tutorial_hidden_cut/metadata.json +++ b/demonstrations_v2/tutorial_hidden_cut/metadata.json @@ -8,7 +8,7 @@ "executable_stable": true, "executable_latest": true, "dateOfPublication": "2025-07-25T10:00:00+00:00", - "dateOfLastModification": "2025-09-09T17:41:50+00:00", + "dateOfLastModification": "2025-09-22T15:48:14+00:00", "categories": [ "Algorithms" ], diff --git a/demonstrations_v2/tutorial_how_to_build_compressed_double_factorized_hamiltonians/metadata.json b/demonstrations_v2/tutorial_how_to_build_compressed_double_factorized_hamiltonians/metadata.json index f72d883b22..65e744d58b 100644 --- a/demonstrations_v2/tutorial_how_to_build_compressed_double_factorized_hamiltonians/metadata.json +++ b/demonstrations_v2/tutorial_how_to_build_compressed_double_factorized_hamiltonians/metadata.json @@ -8,7 +8,7 @@ "executable_stable": true, "executable_latest": true, "dateOfPublication": "2025-03-05T09:00:00+00:00", - "dateOfLastModification": "2025-09-09T17:41:50+00:00", + "dateOfLastModification": "2025-09-22T15:48:14+00:00", "categories": [ "Quantum Chemistry", "Algorithms", diff --git a/demonstrations_v2/tutorial_how_to_build_spin_hamiltonians/metadata.json b/demonstrations_v2/tutorial_how_to_build_spin_hamiltonians/metadata.json index f782b4276f..3e69b0f39a 100644 --- a/demonstrations_v2/tutorial_how_to_build_spin_hamiltonians/metadata.json +++ b/demonstrations_v2/tutorial_how_to_build_spin_hamiltonians/metadata.json @@ -11,7 +11,7 @@ "executable_stable": true, "executable_latest": true, "dateOfPublication": "2024-12-05T00:00:00+00:00", - "dateOfLastModification": "2025-09-09T17:41:50+00:00", + "dateOfLastModification": "2025-09-22T15:48:14+00:00", "categories": [ "Getting Started", "How-to" diff --git a/demonstrations_v2/tutorial_how_to_collect_mcm_stats/metadata.json b/demonstrations_v2/tutorial_how_to_collect_mcm_stats/metadata.json index 2a3dd1a654..46af1e8c0a 100644 --- a/demonstrations_v2/tutorial_how_to_collect_mcm_stats/metadata.json +++ b/demonstrations_v2/tutorial_how_to_collect_mcm_stats/metadata.json @@ -8,7 +8,7 @@ "executable_stable": true, "executable_latest": true, "dateOfPublication": "2024-04-26T00:00:00+00:00", - "dateOfLastModification": "2025-09-09T17:41:50+00:00", + "dateOfLastModification": "2025-09-22T15:48:14+00:00", "categories": [ "Getting Started", "Quantum Computing", diff --git a/demonstrations_v2/tutorial_how_to_create_dynamic_mcm_circuits/metadata.json b/demonstrations_v2/tutorial_how_to_create_dynamic_mcm_circuits/metadata.json index d45c679b7c..d392b6d5ac 100644 --- a/demonstrations_v2/tutorial_how_to_create_dynamic_mcm_circuits/metadata.json +++ b/demonstrations_v2/tutorial_how_to_create_dynamic_mcm_circuits/metadata.json @@ -8,7 +8,7 @@ "executable_stable": true, "executable_latest": true, "dateOfPublication": "2024-05-03T00:00:00+00:00", - "dateOfLastModification": "2025-09-09T17:41:50+00:00", + "dateOfLastModification": "2025-09-22T15:48:14+00:00", "categories": [ "Getting Started", "Quantum Computing", diff --git a/demonstrations_v2/tutorial_how_to_import_qiskit_noise_models/metadata.json b/demonstrations_v2/tutorial_how_to_import_qiskit_noise_models/metadata.json index d0ccf24111..8e8fa1d4fe 100644 --- a/demonstrations_v2/tutorial_how_to_import_qiskit_noise_models/metadata.json +++ b/demonstrations_v2/tutorial_how_to_import_qiskit_noise_models/metadata.json @@ -8,7 +8,7 @@ "executable_stable": true, "executable_latest": true, "dateOfPublication": "2024-11-25T00:00:00+00:00", - "dateOfLastModification": "2025-09-09T17:41:50+00:00", + "dateOfLastModification": "2025-09-22T15:48:14+00:00", "categories": [ "Quantum Computing", "How-to" diff --git a/demonstrations_v2/tutorial_how_to_quantum_just_in_time_compile_vqe_catalyst/demo.py b/demonstrations_v2/tutorial_how_to_quantum_just_in_time_compile_vqe_catalyst/demo.py index 635de43bd5..cbb77ce991 100644 --- a/demonstrations_v2/tutorial_how_to_quantum_just_in_time_compile_vqe_catalyst/demo.py +++ b/demonstrations_v2/tutorial_how_to_quantum_just_in_time_compile_vqe_catalyst/demo.py @@ -1,7 +1,7 @@ r"""How to quantum just-in-time compile VQE with Catalyst ===================================================== -The `Variational Quantum Eigensolver `__ (VQE) is +The :doc:`Variational Quantum Eigensolver ` (VQE) is a widely used quantum algorithm with applications in quantum chemistry and portfolio optimization problems. It is an application of the `Ritz variational principle `__, where a quantum computer is trained to diff --git a/demonstrations_v2/tutorial_how_to_quantum_just_in_time_compile_vqe_catalyst/metadata.json b/demonstrations_v2/tutorial_how_to_quantum_just_in_time_compile_vqe_catalyst/metadata.json index 59607d0ebf..0adda8ceed 100644 --- a/demonstrations_v2/tutorial_how_to_quantum_just_in_time_compile_vqe_catalyst/metadata.json +++ b/demonstrations_v2/tutorial_how_to_quantum_just_in_time_compile_vqe_catalyst/metadata.json @@ -11,7 +11,7 @@ "executable_stable": true, "executable_latest": true, "dateOfPublication": "2024-04-26T00:00:00+00:00", - "dateOfLastModification": "2025-09-09T17:41:50+00:00", + "dateOfLastModification": "2025-09-22T15:48:14+00:00", "categories": [ "Quantum Machine Learning", "Optimization", diff --git a/demonstrations_v2/tutorial_how_to_use_noise_models/metadata.json b/demonstrations_v2/tutorial_how_to_use_noise_models/metadata.json index 4c8a46580a..f75808a784 100644 --- a/demonstrations_v2/tutorial_how_to_use_noise_models/metadata.json +++ b/demonstrations_v2/tutorial_how_to_use_noise_models/metadata.json @@ -8,7 +8,7 @@ "executable_stable": true, "executable_latest": true, "dateOfPublication": "2024-10-01T00:00:00+00:00", - "dateOfLastModification": "2025-09-09T17:41:50+00:00", + "dateOfLastModification": "2025-09-22T15:48:14+00:00", "categories": [ "Quantum Computing", "How-to" diff --git a/demonstrations_v2/tutorial_how_to_use_qualtran_with_pennylane/metadata.json b/demonstrations_v2/tutorial_how_to_use_qualtran_with_pennylane/metadata.json index 61b67e849b..fef1caa138 100644 --- a/demonstrations_v2/tutorial_how_to_use_qualtran_with_pennylane/metadata.json +++ b/demonstrations_v2/tutorial_how_to_use_qualtran_with_pennylane/metadata.json @@ -8,7 +8,7 @@ "executable_stable": true, "executable_latest": true, "dateOfPublication": "2025-08-18T00:00:00+00:00", - "dateOfLastModification": "2025-09-09T17:41:50+00:00", + "dateOfLastModification": "2025-09-22T15:48:14+00:00", "categories": [ "Quantum Computing", "How-to" diff --git a/demonstrations_v2/tutorial_how_to_use_quantum_arithmetic_operators/metadata.json b/demonstrations_v2/tutorial_how_to_use_quantum_arithmetic_operators/metadata.json index 8985ec000d..c02985d75c 100644 --- a/demonstrations_v2/tutorial_how_to_use_quantum_arithmetic_operators/metadata.json +++ b/demonstrations_v2/tutorial_how_to_use_quantum_arithmetic_operators/metadata.json @@ -11,7 +11,7 @@ "executable_stable": true, "executable_latest": true, "dateOfPublication": "2024-11-05T00:00:00+00:00", - "dateOfLastModification": "2025-09-09T17:41:50+00:00", + "dateOfLastModification": "2025-09-22T15:48:14+00:00", "categories": [ "Quantum Computing", "Algorithms", diff --git a/demonstrations_v2/tutorial_how_to_use_registers/metadata.json b/demonstrations_v2/tutorial_how_to_use_registers/metadata.json index d21e7f0941..795e3c07d6 100644 --- a/demonstrations_v2/tutorial_how_to_use_registers/metadata.json +++ b/demonstrations_v2/tutorial_how_to_use_registers/metadata.json @@ -8,7 +8,7 @@ "executable_stable": true, "executable_latest": true, "dateOfPublication": "2024-09-05T00:00:00+00:00", - "dateOfLastModification": "2025-09-09T17:41:50+00:00", + "dateOfLastModification": "2025-09-22T15:48:14+00:00", "categories": [ "Getting Started", "How-to" diff --git a/demonstrations_v2/tutorial_implicit_diff_susceptibility/metadata.json b/demonstrations_v2/tutorial_implicit_diff_susceptibility/metadata.json index d4bbd393e5..8af2949f82 100644 --- a/demonstrations_v2/tutorial_implicit_diff_susceptibility/metadata.json +++ b/demonstrations_v2/tutorial_implicit_diff_susceptibility/metadata.json @@ -11,7 +11,7 @@ "executable_stable": true, "executable_latest": true, "dateOfPublication": "2022-11-28T00:00:00+00:00", - "dateOfLastModification": "2025-09-09T17:41:50+00:00", + "dateOfLastModification": "2025-09-22T15:48:14+00:00", "categories": [ "Optimization" ], diff --git a/demonstrations_v2/tutorial_initial_state_preparation/metadata.json b/demonstrations_v2/tutorial_initial_state_preparation/metadata.json index eaf136111b..553515013a 100644 --- a/demonstrations_v2/tutorial_initial_state_preparation/metadata.json +++ b/demonstrations_v2/tutorial_initial_state_preparation/metadata.json @@ -8,7 +8,7 @@ "executable_stable": true, "executable_latest": true, "dateOfPublication": "2023-10-20T00:00:00+00:00", - "dateOfLastModification": "2025-09-09T17:41:50+00:00", + "dateOfLastModification": "2025-09-22T15:48:14+00:00", "categories": [ "Quantum Chemistry" ], diff --git a/demonstrations_v2/tutorial_intro_amplitude_amplification/metadata.json b/demonstrations_v2/tutorial_intro_amplitude_amplification/metadata.json index fae7ad2638..a7977e5ed3 100644 --- a/demonstrations_v2/tutorial_intro_amplitude_amplification/metadata.json +++ b/demonstrations_v2/tutorial_intro_amplitude_amplification/metadata.json @@ -11,7 +11,7 @@ "executable_stable": true, "executable_latest": true, "dateOfPublication": "2024-05-07T00:00:00+00:00", - "dateOfLastModification": "2025-09-09T17:41:50+00:00", + "dateOfLastModification": "2025-09-22T15:48:14+00:00", "categories": [ "Quantum Computing", "Algorithms" diff --git a/demonstrations_v2/tutorial_intro_qrom/metadata.json b/demonstrations_v2/tutorial_intro_qrom/metadata.json index 7ad0ffd1bf..0de956d6c5 100644 --- a/demonstrations_v2/tutorial_intro_qrom/metadata.json +++ b/demonstrations_v2/tutorial_intro_qrom/metadata.json @@ -11,7 +11,7 @@ "executable_stable": true, "executable_latest": true, "dateOfPublication": "2024-09-18T00:00:00+00:00", - "dateOfLastModification": "2025-09-09T17:41:50+00:00", + "dateOfLastModification": "2025-09-22T15:48:14+00:00", "categories": [ "Algorithms", "Quantum Computing" diff --git a/demonstrations_v2/tutorial_intro_qsvt/metadata.json b/demonstrations_v2/tutorial_intro_qsvt/metadata.json index 8c226d4e53..98b6be8e13 100644 --- a/demonstrations_v2/tutorial_intro_qsvt/metadata.json +++ b/demonstrations_v2/tutorial_intro_qsvt/metadata.json @@ -8,7 +8,7 @@ "executable_stable": true, "executable_latest": true, "dateOfPublication": "2023-05-23T00:00:00+00:00", - "dateOfLastModification": "2025-09-09T17:41:50+00:00", + "dateOfLastModification": "2025-09-22T15:48:14+00:00", "categories": [ "Algorithms", "Quantum Computing" diff --git a/demonstrations_v2/tutorial_iqp_circuit_optimization_jax/metadata.json b/demonstrations_v2/tutorial_iqp_circuit_optimization_jax/metadata.json index aceef6c19e..2bb1023cc2 100644 --- a/demonstrations_v2/tutorial_iqp_circuit_optimization_jax/metadata.json +++ b/demonstrations_v2/tutorial_iqp_circuit_optimization_jax/metadata.json @@ -11,7 +11,7 @@ "executable_stable": true, "executable_latest": true, "dateOfPublication": "2025-02-14T09:00:00+00:00", - "dateOfLastModification": "2025-09-09T17:41:50+00:00", + "dateOfLastModification": "2025-09-22T15:48:14+00:00", "categories": [ "Optimization", "Algorithms" diff --git a/demonstrations_v2/tutorial_isingmodel_PyTorch/metadata.json b/demonstrations_v2/tutorial_isingmodel_PyTorch/metadata.json index 992c187861..c18c7ff855 100644 --- a/demonstrations_v2/tutorial_isingmodel_PyTorch/metadata.json +++ b/demonstrations_v2/tutorial_isingmodel_PyTorch/metadata.json @@ -8,7 +8,7 @@ "executable_stable": true, "executable_latest": true, "dateOfPublication": "2019-10-16T00:00:00+00:00", - "dateOfLastModification": "2025-09-09T17:41:50+00:00", + "dateOfLastModification": "2025-09-22T15:48:14+00:00", "categories": [ "Optimization" ], diff --git a/demonstrations_v2/tutorial_jax_transformations/metadata.json b/demonstrations_v2/tutorial_jax_transformations/metadata.json index 0438acee01..3c1d8416fd 100644 --- a/demonstrations_v2/tutorial_jax_transformations/metadata.json +++ b/demonstrations_v2/tutorial_jax_transformations/metadata.json @@ -8,7 +8,7 @@ "executable_stable": true, "executable_latest": true, "dateOfPublication": "2021-04-12T00:00:00+00:00", - "dateOfLastModification": "2025-09-09T17:41:50+00:00", + "dateOfLastModification": "2025-09-22T15:48:14+00:00", "categories": [ "Devices and Performance" ], diff --git a/demonstrations_v2/tutorial_kak_decomposition/metadata.json b/demonstrations_v2/tutorial_kak_decomposition/metadata.json index 43438f66ba..3cc6bdfe96 100644 --- a/demonstrations_v2/tutorial_kak_decomposition/metadata.json +++ b/demonstrations_v2/tutorial_kak_decomposition/metadata.json @@ -8,7 +8,7 @@ "executable_stable": true, "executable_latest": true, "dateOfPublication": "2024-11-25T00:00:00+00:00", - "dateOfLastModification": "2025-09-09T17:41:50+00:00", + "dateOfLastModification": "2025-09-22T15:48:14+00:00", "categories": [ "Quantum Computing", "Algorithms" diff --git a/demonstrations_v2/tutorial_kernel_based_training/demo.py b/demonstrations_v2/tutorial_kernel_based_training/demo.py index 001a9504b4..6a472f2a8b 100644 --- a/demonstrations_v2/tutorial_kernel_based_training/demo.py +++ b/demonstrations_v2/tutorial_kernel_based_training/demo.py @@ -28,13 +28,13 @@ `Schuld (2021) `__). The link between quantum models and kernel methods has important practical implications: -we can replace the common `variational approach `__ +we can replace the common `variational approach `__ to quantum machine learning with a classical kernel method where the kernel—a small building block of the overall algorithm—is computed by a quantum device. In many situations there are guarantees that we get better or at least equally good results. This demonstration explores how kernel-based training compares with -`variational training `__ in terms of the number of quantum +:doc:`variational training ` in terms of the number of quantum circuits that have to be evaluated. For this we train a quantum machine learning model with a kernel-based approach using a combination of PennyLane and the `scikit-learn `__ machine @@ -84,7 +84,7 @@ # # where :math:`| \phi(x)\rangle` is prepared # by a fixed `embedding -# circuit `__ that +# circuit `__ that # encodes data inputs :math:`x,` # and :math:`\mathcal{M}` is an arbitrary observable. This model includes variational # quantum machine learning models, since the observable can @@ -369,7 +369,7 @@ def circuit_evals_kernel(n_data, split): # as possible. For this we use a bias term in the quantum model, and train # on the hinge loss. # -# We also explicitly use the `parameter-shift `__ +# We also explicitly use the `parameter-shift `__ # differentiation method in the quantum node, since this is a method which works on hardware as well. # While ``diff_method='backprop'`` or ``diff_method='adjoint'`` would reduce the number of # circuit evaluations significantly, they are based on tricks that are only suitable for simulators, diff --git a/demonstrations_v2/tutorial_kernel_based_training/metadata.json b/demonstrations_v2/tutorial_kernel_based_training/metadata.json index 4af0c5cfd0..e0ae767c80 100644 --- a/demonstrations_v2/tutorial_kernel_based_training/metadata.json +++ b/demonstrations_v2/tutorial_kernel_based_training/metadata.json @@ -8,7 +8,7 @@ "executable_stable": true, "executable_latest": true, "dateOfPublication": "2021-02-03T00:00:00+00:00", - "dateOfLastModification": "2025-09-09T17:41:50+00:00", + "dateOfLastModification": "2025-09-22T15:48:14+00:00", "categories": [ "Quantum Machine Learning" ], diff --git a/demonstrations_v2/tutorial_kernels_module/demo.py b/demonstrations_v2/tutorial_kernels_module/demo.py index 3598ef29a8..d240331775 100644 --- a/demonstrations_v2/tutorial_kernels_module/demo.py +++ b/demonstrations_v2/tutorial_kernels_module/demo.py @@ -316,7 +316,7 @@ def kernel(x1, x2, params): # to use the observable type # `Projector `__. # This is shown in the -# `demo on kernel-based training of quantum models `__, where you will also find more +# :doc:`demo on kernel-based training of quantum models `, where you will also find more # background information on the kernel circuit structure itself. # # Before focusing on the kernel values we have to provide values for the diff --git a/demonstrations_v2/tutorial_kernels_module/metadata.json b/demonstrations_v2/tutorial_kernels_module/metadata.json index 76bd313e07..876f96686d 100644 --- a/demonstrations_v2/tutorial_kernels_module/metadata.json +++ b/demonstrations_v2/tutorial_kernels_module/metadata.json @@ -23,7 +23,7 @@ "executable_stable": true, "executable_latest": true, "dateOfPublication": "2021-06-24T00:00:00+00:00", - "dateOfLastModification": "2025-09-09T17:41:50+00:00", + "dateOfLastModification": "2025-09-22T15:48:14+00:00", "categories": [ "Quantum Machine Learning" ], diff --git a/demonstrations_v2/tutorial_lcu_blockencoding/metadata.json b/demonstrations_v2/tutorial_lcu_blockencoding/metadata.json index 7ca607b53e..db06e00e1a 100644 --- a/demonstrations_v2/tutorial_lcu_blockencoding/metadata.json +++ b/demonstrations_v2/tutorial_lcu_blockencoding/metadata.json @@ -14,7 +14,7 @@ "executable_stable": true, "executable_latest": true, "dateOfPublication": "2023-10-25T00:00:00+00:00", - "dateOfLastModification": "2025-09-09T17:41:50+00:00", + "dateOfLastModification": "2025-09-22T15:48:14+00:00", "categories": [ "Algorithms", "Quantum Computing" diff --git a/demonstrations_v2/tutorial_learning_dynamics_incoherently/metadata.json b/demonstrations_v2/tutorial_learning_dynamics_incoherently/metadata.json index 916198dff9..0e0b434512 100644 --- a/demonstrations_v2/tutorial_learning_dynamics_incoherently/metadata.json +++ b/demonstrations_v2/tutorial_learning_dynamics_incoherently/metadata.json @@ -8,7 +8,7 @@ "executable_stable": true, "executable_latest": true, "dateOfPublication": "2024-08-15T00:00:00+00:00", - "dateOfLastModification": "2025-09-09T17:41:50+00:00", + "dateOfLastModification": "2025-09-22T15:48:14+00:00", "categories": [ "Quantum Machine Learning", "How-to" diff --git a/demonstrations_v2/tutorial_learning_few_data/metadata.json b/demonstrations_v2/tutorial_learning_few_data/metadata.json index 7d80d8f944..92a1a3a2bb 100644 --- a/demonstrations_v2/tutorial_learning_few_data/metadata.json +++ b/demonstrations_v2/tutorial_learning_few_data/metadata.json @@ -14,7 +14,7 @@ "executable_stable": true, "executable_latest": true, "dateOfPublication": "2022-08-29T00:00:00+00:00", - "dateOfLastModification": "2025-09-09T17:41:50+00:00", + "dateOfLastModification": "2025-09-22T15:48:14+00:00", "categories": [ "Quantum Machine Learning" ], diff --git a/demonstrations_v2/tutorial_learning_from_experiments/metadata.json b/demonstrations_v2/tutorial_learning_from_experiments/metadata.json index fbe3ac13fc..edfeafbfc2 100644 --- a/demonstrations_v2/tutorial_learning_from_experiments/metadata.json +++ b/demonstrations_v2/tutorial_learning_from_experiments/metadata.json @@ -8,7 +8,7 @@ "executable_stable": true, "executable_latest": true, "dateOfPublication": "2022-04-18T00:00:00+00:00", - "dateOfLastModification": "2025-09-09T17:41:50+00:00", + "dateOfLastModification": "2025-09-22T15:48:14+00:00", "categories": [ "Quantum Machine Learning" ], diff --git a/demonstrations_v2/tutorial_learningshallow/metadata.json b/demonstrations_v2/tutorial_learningshallow/metadata.json index 08ae9a26fd..f6da8b5eaa 100644 --- a/demonstrations_v2/tutorial_learningshallow/metadata.json +++ b/demonstrations_v2/tutorial_learningshallow/metadata.json @@ -8,7 +8,7 @@ "executable_stable": true, "executable_latest": true, "dateOfPublication": "2024-01-24T00:00:00+00:00", - "dateOfLastModification": "2025-09-09T17:41:50+00:00", + "dateOfLastModification": "2025-09-22T15:48:14+00:00", "categories": [ "Optimization", "Quantum Computing", diff --git a/demonstrations_v2/tutorial_liealgebra/metadata.json b/demonstrations_v2/tutorial_liealgebra/metadata.json index f7cf8b29ef..34d266c35b 100644 --- a/demonstrations_v2/tutorial_liealgebra/metadata.json +++ b/demonstrations_v2/tutorial_liealgebra/metadata.json @@ -8,7 +8,7 @@ "executable_stable": true, "executable_latest": true, "dateOfPublication": "2024-02-27T00:00:00+00:00", - "dateOfLastModification": "2025-09-09T17:41:50+00:00", + "dateOfLastModification": "2025-09-22T15:48:14+00:00", "categories": [ "Quantum Computing", "Getting Started" diff --git a/demonstrations_v2/tutorial_liesim/metadata.json b/demonstrations_v2/tutorial_liesim/metadata.json index cf9487e7c7..7b2680231c 100644 --- a/demonstrations_v2/tutorial_liesim/metadata.json +++ b/demonstrations_v2/tutorial_liesim/metadata.json @@ -8,7 +8,7 @@ "executable_stable": true, "executable_latest": true, "dateOfPublication": "2024-06-07T00:00:00+00:00", - "dateOfLastModification": "2025-09-09T17:41:50+00:00", + "dateOfLastModification": "2025-09-22T15:48:14+00:00", "categories": [ "Quantum Computing", "Getting Started" diff --git a/demonstrations_v2/tutorial_liesim_extension/metadata.json b/demonstrations_v2/tutorial_liesim_extension/metadata.json index 2298e636d4..f20d933038 100644 --- a/demonstrations_v2/tutorial_liesim_extension/metadata.json +++ b/demonstrations_v2/tutorial_liesim_extension/metadata.json @@ -8,7 +8,7 @@ "executable_stable": true, "executable_latest": true, "dateOfPublication": "2024-06-18T00:00:00+00:00", - "dateOfLastModification": "2025-09-09T17:41:50+00:00", + "dateOfLastModification": "2025-09-22T15:48:14+00:00", "categories": [ "Quantum Computing", "Quantum Machine Learning" diff --git a/demonstrations_v2/tutorial_local_cost_functions/metadata.json b/demonstrations_v2/tutorial_local_cost_functions/metadata.json index af30438b07..1f7610be20 100644 --- a/demonstrations_v2/tutorial_local_cost_functions/metadata.json +++ b/demonstrations_v2/tutorial_local_cost_functions/metadata.json @@ -8,7 +8,7 @@ "executable_stable": true, "executable_latest": true, "dateOfPublication": "2020-09-09T00:00:00+00:00", - "dateOfLastModification": "2025-09-09T17:41:50+00:00", + "dateOfLastModification": "2025-09-22T15:48:14+00:00", "categories": [ "Optimization" ], diff --git a/demonstrations_v2/tutorial_loom_catalyst/metadata.json b/demonstrations_v2/tutorial_loom_catalyst/metadata.json index cbcfdaa694..2a41af3d3c 100644 --- a/demonstrations_v2/tutorial_loom_catalyst/metadata.json +++ b/demonstrations_v2/tutorial_loom_catalyst/metadata.json @@ -11,7 +11,7 @@ "executable_stable": false, "executable_latest": false, "dateOfPublication": "2025-08-29T14:00:00+00:00", - "dateOfLastModification": "2025-08-29T14:00:00+00:01", + "dateOfLastModification": "2025-09-22T15:48:14+00:00", "categories": [ "Quantum Computing", "Quantum Hardware" @@ -29,7 +29,8 @@ ], "seoDescription": "Learn how to implement a quantum error correction protocol using Catalyst and Loom", "doi": "", - "references": [ { + "references": [ + { "id": "Munro2013", "type": "article", "title": "Quantum Error Correction for Beginners", @@ -39,8 +40,7 @@ "url": "https://arxiv.org/abs/0905.2794" } ], - "basedOnPapers": [ - ], + "basedOnPapers": [], "referencedByPapers": [], "relatedContent": [ { diff --git a/demonstrations_v2/tutorial_magic_state_distillation/metadata.json b/demonstrations_v2/tutorial_magic_state_distillation/metadata.json index 88d41cce16..28ec7547df 100644 --- a/demonstrations_v2/tutorial_magic_state_distillation/metadata.json +++ b/demonstrations_v2/tutorial_magic_state_distillation/metadata.json @@ -8,7 +8,7 @@ "executable_stable": true, "executable_latest": true, "dateOfPublication": "2024-04-26T00:00:00+00:00", - "dateOfLastModification": "2025-09-09T17:41:50+00:00", + "dateOfLastModification": "2025-09-22T15:48:14+00:00", "categories": [ "Algorithms", "Quantum Computing" diff --git a/demonstrations_v2/tutorial_mapping/metadata.json b/demonstrations_v2/tutorial_mapping/metadata.json index b98215a406..fbb0c1de4d 100644 --- a/demonstrations_v2/tutorial_mapping/metadata.json +++ b/demonstrations_v2/tutorial_mapping/metadata.json @@ -8,7 +8,7 @@ "executable_stable": true, "executable_latest": true, "dateOfPublication": "2024-05-06T00:00:00+00:00", - "dateOfLastModification": "2025-09-09T17:41:50+00:00", + "dateOfLastModification": "2025-09-22T15:48:14+00:00", "categories": [ "Algorithms", "Quantum Computing", diff --git a/demonstrations_v2/tutorial_mbqc/metadata.json b/demonstrations_v2/tutorial_mbqc/metadata.json index 3ec89fdd53..6aa8e485ef 100644 --- a/demonstrations_v2/tutorial_mbqc/metadata.json +++ b/demonstrations_v2/tutorial_mbqc/metadata.json @@ -11,7 +11,7 @@ "executable_stable": true, "executable_latest": true, "dateOfPublication": "2022-12-05T00:00:00+00:00", - "dateOfLastModification": "2025-09-09T17:41:50+00:00", + "dateOfLastModification": "2025-09-22T15:48:14+00:00", "categories": [ "Quantum Hardware", "Quantum Computing" diff --git a/demonstrations_v2/tutorial_mcm_introduction/metadata.json b/demonstrations_v2/tutorial_mcm_introduction/metadata.json index 48c6688035..072e534231 100644 --- a/demonstrations_v2/tutorial_mcm_introduction/metadata.json +++ b/demonstrations_v2/tutorial_mcm_introduction/metadata.json @@ -8,7 +8,7 @@ "executable_stable": true, "executable_latest": true, "dateOfPublication": "2024-05-10T00:00:00+00:00", - "dateOfLastModification": "2025-09-09T17:41:50+00:00", + "dateOfLastModification": "2025-09-22T15:48:14+00:00", "categories": [ "Getting Started", "Quantum Computing" diff --git a/demonstrations_v2/tutorial_measurement_optimize/metadata.json b/demonstrations_v2/tutorial_measurement_optimize/metadata.json index dd48fe7084..7838f64b81 100644 --- a/demonstrations_v2/tutorial_measurement_optimize/metadata.json +++ b/demonstrations_v2/tutorial_measurement_optimize/metadata.json @@ -8,7 +8,7 @@ "executable_stable": true, "executable_latest": true, "dateOfPublication": "2021-01-18T00:00:00+00:00", - "dateOfLastModification": "2025-09-09T17:41:50+00:00", + "dateOfLastModification": "2025-09-22T15:48:14+00:00", "categories": [ "Quantum Chemistry" ], diff --git a/demonstrations_v2/tutorial_mitigation_advantage/metadata.json b/demonstrations_v2/tutorial_mitigation_advantage/metadata.json index 0c1095c2f2..041294f9a6 100644 --- a/demonstrations_v2/tutorial_mitigation_advantage/metadata.json +++ b/demonstrations_v2/tutorial_mitigation_advantage/metadata.json @@ -8,7 +8,7 @@ "executable_stable": true, "executable_latest": true, "dateOfPublication": "2023-06-16T00:00:00+00:00", - "dateOfLastModification": "2025-09-09T17:41:50+00:00", + "dateOfLastModification": "2025-09-22T15:48:14+00:00", "categories": [ "Quantum Hardware", "Quantum Computing" diff --git a/demonstrations_v2/tutorial_mol_geo_opt/metadata.json b/demonstrations_v2/tutorial_mol_geo_opt/metadata.json index fd0478b56b..5d9c500c9b 100644 --- a/demonstrations_v2/tutorial_mol_geo_opt/metadata.json +++ b/demonstrations_v2/tutorial_mol_geo_opt/metadata.json @@ -8,7 +8,7 @@ "executable_stable": true, "executable_latest": true, "dateOfPublication": "2021-06-30T00:00:00+00:00", - "dateOfLastModification": "2025-09-09T17:41:50+00:00", + "dateOfLastModification": "2025-09-22T15:48:14+00:00", "categories": [ "Quantum Chemistry" ], diff --git a/demonstrations_v2/tutorial_mps/metadata.json b/demonstrations_v2/tutorial_mps/metadata.json index 6fea7254ff..1de2f98d8e 100644 --- a/demonstrations_v2/tutorial_mps/metadata.json +++ b/demonstrations_v2/tutorial_mps/metadata.json @@ -8,7 +8,7 @@ "executable_stable": true, "executable_latest": true, "dateOfPublication": "2024-09-29T00:00:00+00:00", - "dateOfLastModification": "2025-09-09T17:41:50+00:00", + "dateOfLastModification": "2025-09-22T15:48:14+00:00", "categories": [ "Quantum Computing", "Algorithms", diff --git a/demonstrations_v2/tutorial_multiclass_classification/metadata.json b/demonstrations_v2/tutorial_multiclass_classification/metadata.json index 2a9e58928c..6b0eb2c8d1 100644 --- a/demonstrations_v2/tutorial_multiclass_classification/metadata.json +++ b/demonstrations_v2/tutorial_multiclass_classification/metadata.json @@ -8,7 +8,7 @@ "executable_stable": true, "executable_latest": true, "dateOfPublication": "2020-04-09T00:00:00+00:00", - "dateOfLastModification": "2024-11-06T00:00:00+00:00", + "dateOfLastModification": "2025-09-22T15:48:14+00:00", "categories": [ "Algorithms" ], @@ -36,4 +36,4 @@ "weight": 1.0 } ] -} +} \ No newline at end of file diff --git a/demonstrations_v2/tutorial_neutral_atoms/metadata.json b/demonstrations_v2/tutorial_neutral_atoms/metadata.json index 42cf34703b..53f14d2224 100644 --- a/demonstrations_v2/tutorial_neutral_atoms/metadata.json +++ b/demonstrations_v2/tutorial_neutral_atoms/metadata.json @@ -8,7 +8,7 @@ "executable_stable": true, "executable_latest": true, "dateOfPublication": "2023-05-30T00:00:00+00:00", - "dateOfLastModification": "2025-09-09T17:41:50+00:00", + "dateOfLastModification": "2025-09-22T15:48:14+00:00", "categories": [ "Quantum Hardware", "Quantum Computing" diff --git a/demonstrations_v2/tutorial_noisy_circuit_optimization/metadata.json b/demonstrations_v2/tutorial_noisy_circuit_optimization/metadata.json index ce7af0e77b..7aa765eed0 100644 --- a/demonstrations_v2/tutorial_noisy_circuit_optimization/metadata.json +++ b/demonstrations_v2/tutorial_noisy_circuit_optimization/metadata.json @@ -8,7 +8,7 @@ "executable_stable": true, "executable_latest": true, "dateOfPublication": "2020-06-01T00:00:00+00:00", - "dateOfLastModification": "2025-09-09T17:41:50+00:00", + "dateOfLastModification": "2025-09-22T15:48:14+00:00", "categories": [ "Devices and Performance" ], diff --git a/demonstrations_v2/tutorial_noisy_circuits/metadata.json b/demonstrations_v2/tutorial_noisy_circuits/metadata.json index 411e2f1a25..f3881691b8 100644 --- a/demonstrations_v2/tutorial_noisy_circuits/metadata.json +++ b/demonstrations_v2/tutorial_noisy_circuits/metadata.json @@ -8,7 +8,7 @@ "executable_stable": true, "executable_latest": true, "dateOfPublication": "2021-02-22T00:00:00+00:00", - "dateOfLastModification": "2025-09-09T17:41:50+00:00", + "dateOfLastModification": "2025-09-22T15:48:14+00:00", "categories": [ "Getting Started" ], diff --git a/demonstrations_v2/tutorial_odegen/metadata.json b/demonstrations_v2/tutorial_odegen/metadata.json index cbceba97e0..607e117847 100644 --- a/demonstrations_v2/tutorial_odegen/metadata.json +++ b/demonstrations_v2/tutorial_odegen/metadata.json @@ -8,7 +8,7 @@ "executable_stable": true, "executable_latest": true, "dateOfPublication": "2023-12-12T00:00:00+00:00", - "dateOfLastModification": "2025-09-09T17:41:50+00:00", + "dateOfLastModification": "2025-09-22T15:48:14+00:00", "categories": [ "Optimization", "Quantum Computing", diff --git a/demonstrations_v2/tutorial_optimal_control/metadata.json b/demonstrations_v2/tutorial_optimal_control/metadata.json index 2bae7c5886..99315d02db 100644 --- a/demonstrations_v2/tutorial_optimal_control/metadata.json +++ b/demonstrations_v2/tutorial_optimal_control/metadata.json @@ -8,7 +8,7 @@ "executable_stable": true, "executable_latest": true, "dateOfPublication": "2023-08-08T00:00:00+00:00", - "dateOfLastModification": "2025-09-09T17:41:50+00:00", + "dateOfLastModification": "2025-09-22T15:48:14+00:00", "categories": [ "Optimization", "Quantum Computing", diff --git a/demonstrations_v2/tutorial_pasqal/metadata.json b/demonstrations_v2/tutorial_pasqal/metadata.json index 745d4a71ac..b62dbb41f0 100644 --- a/demonstrations_v2/tutorial_pasqal/metadata.json +++ b/demonstrations_v2/tutorial_pasqal/metadata.json @@ -8,7 +8,7 @@ "executable_stable": true, "executable_latest": true, "dateOfPublication": "2020-10-13T00:00:00+00:00", - "dateOfLastModification": "2025-09-09T17:41:50+00:00", + "dateOfLastModification": "2025-09-22T15:48:14+00:00", "categories": [ "Quantum Hardware", "Quantum Computing" diff --git a/demonstrations_v2/tutorial_period_finding/metadata.json b/demonstrations_v2/tutorial_period_finding/metadata.json index 4810f0c287..265701c483 100644 --- a/demonstrations_v2/tutorial_period_finding/metadata.json +++ b/demonstrations_v2/tutorial_period_finding/metadata.json @@ -8,7 +8,7 @@ "executable_stable": true, "executable_latest": true, "dateOfPublication": "2025-04-16T10:00:00+00:00", - "dateOfLastModification": "2025-09-09T17:41:50+00:00", + "dateOfLastModification": "2025-09-22T15:48:14+00:00", "categories": [ "Algorithms" ], diff --git a/demonstrations_v2/tutorial_phase_kickback/metadata.json b/demonstrations_v2/tutorial_phase_kickback/metadata.json index 220eeaba5d..cc5592c8a9 100644 --- a/demonstrations_v2/tutorial_phase_kickback/metadata.json +++ b/demonstrations_v2/tutorial_phase_kickback/metadata.json @@ -8,7 +8,7 @@ "executable_stable": true, "executable_latest": true, "dateOfPublication": "2023-08-01T00:00:00+00:00", - "dateOfLastModification": "2025-09-09T17:41:50+00:00", + "dateOfLastModification": "2025-09-22T15:48:14+00:00", "categories": [ "Getting Started", "Algorithms", diff --git a/demonstrations_v2/tutorial_photonics/metadata.json b/demonstrations_v2/tutorial_photonics/metadata.json index cbeca85dde..1f0058bc51 100644 --- a/demonstrations_v2/tutorial_photonics/metadata.json +++ b/demonstrations_v2/tutorial_photonics/metadata.json @@ -8,7 +8,7 @@ "executable_stable": true, "executable_latest": true, "dateOfPublication": "2022-05-31T00:00:00+00:00", - "dateOfLastModification": "2025-09-09T17:41:50+00:00", + "dateOfLastModification": "2025-09-22T15:48:14+00:00", "categories": [ "Quantum Hardware", "Quantum Computing" diff --git a/demonstrations_v2/tutorial_post-variational_quantum_neural_networks/metadata.json b/demonstrations_v2/tutorial_post-variational_quantum_neural_networks/metadata.json index 5fe0fa6d3a..5b17127241 100644 --- a/demonstrations_v2/tutorial_post-variational_quantum_neural_networks/metadata.json +++ b/demonstrations_v2/tutorial_post-variational_quantum_neural_networks/metadata.json @@ -11,7 +11,7 @@ "executable_stable": true, "executable_latest": true, "dateOfPublication": "2024-10-07T00:00:00+00:00", - "dateOfLastModification": "2025-09-09T17:41:50+00:00", + "dateOfLastModification": "2025-09-22T15:48:14+00:00", "categories": [ "Quantum Machine Learning", "Algorithms", diff --git a/demonstrations_v2/tutorial_pulse_programming101/demo.py b/demonstrations_v2/tutorial_pulse_programming101/demo.py index 59b3426d13..cd3deac081 100644 --- a/demonstrations_v2/tutorial_pulse_programming101/demo.py +++ b/demonstrations_v2/tutorial_pulse_programming101/demo.py @@ -26,8 +26,8 @@ Pulses in quantum computers --------------------------- -In many quantum computing architectures such as `superconducting `_, `ion trap `_ -and `neutral atom Rydberg `_ systems, +In many quantum computing architectures such as :doc:`superconducting `, :doc:`ion trap ` +and :doc:`neutral atom Rydberg ` systems, qubits are realized through physical systems with a discrete set of energy levels. For example, transmon qubits realize an anharmonic oscillator whose ground and first excited states can serve as the two energy levels of a qubit. Such a qubit can be controlled via an electromagnetic field tuned to its energy gap. In general, this @@ -40,7 +40,7 @@ from an initial state :math:`|\psi(t_0)\rangle` to a final state :math:`|\psi(t_1)\rangle.` This process corresponds to a unitary evolution :math:`U(t_0, t_1)` of the input state from time :math:`t_0` to :math:`t_1,` i.e. :math:`|\psi(t_1)\rangle = U(t_0, t_1) |\psi(t_0)\rangle.` -In most digital quantum computers (with the exception of `measurement-based `_ architectures), the amplitude and frequencies of predefined pulse sequences are +In most digital quantum computers (with the exception of :doc:`measurement-based ` architectures), the amplitude and frequencies of predefined pulse sequences are fine tuned to realize the native gates of the quantum computer. More specifically, the Hamiltonian interaction :math:`H(t)` is tuned such that the respective evolution :math:`U(t_0, t_1)` realizes for example a Pauli or CNOT gate (see e.g. *cross-resonance* gates for superconducting qubits in [#Sheldon2016]_). @@ -220,7 +220,7 @@ def qnode(params): # We can now use the ability to access gradients to perform the variational quantum eigensolver on the pulse level (ctrl-VQE) as is done in [#Mitei]_. # For a more general introduction to VQE, see :doc:`demos/tutorial_vqe`. # First, we define the molecular Hamiltonian whose energy expectation value we want to minimize. This serves as our objective Hamiltonian. -# We are using :math:`\text{HeH}^+` as a simple example and load it from the `PennyLane quantum datasets `_ website. +# We are using :math:`\text{HeH}^+` as a simple example and load it from the `PennyLane quantum datasets `_ website. # We are going to use the tapered Hamiltonian, which makes use of symmetries to reduce the number of qubits, see :doc:`demos/tutorial_qubit_tapering` for details. data = qml.data.load("qchem", molname="HeH+", basis="STO-3G", bondlength=1.5)[0] diff --git a/demonstrations_v2/tutorial_pulse_programming101/metadata.json b/demonstrations_v2/tutorial_pulse_programming101/metadata.json index a81251ffa0..17babb9ffc 100644 --- a/demonstrations_v2/tutorial_pulse_programming101/metadata.json +++ b/demonstrations_v2/tutorial_pulse_programming101/metadata.json @@ -8,7 +8,7 @@ "executable_stable": true, "executable_latest": true, "dateOfPublication": "2023-03-08T00:00:00+00:00", - "dateOfLastModification": "2025-09-09T17:41:50+00:00", + "dateOfLastModification": "2025-09-22T15:48:14+00:00", "categories": [ "Quantum Hardware", "Quantum Computing" diff --git a/demonstrations_v2/tutorial_qaoa_intro/metadata.json b/demonstrations_v2/tutorial_qaoa_intro/metadata.json index f09ef5ea5d..dac985e37e 100644 --- a/demonstrations_v2/tutorial_qaoa_intro/metadata.json +++ b/demonstrations_v2/tutorial_qaoa_intro/metadata.json @@ -8,7 +8,7 @@ "executable_stable": true, "executable_latest": true, "dateOfPublication": "2020-11-18T00:00:00+00:00", - "dateOfLastModification": "2025-09-09T17:41:50+00:00", + "dateOfLastModification": "2025-09-22T15:48:14+00:00", "categories": [ "Optimization" ], diff --git a/demonstrations_v2/tutorial_qaoa_maxcut/metadata.json b/demonstrations_v2/tutorial_qaoa_maxcut/metadata.json index c36401dee1..55697aa304 100644 --- a/demonstrations_v2/tutorial_qaoa_maxcut/metadata.json +++ b/demonstrations_v2/tutorial_qaoa_maxcut/metadata.json @@ -8,7 +8,7 @@ "executable_stable": true, "executable_latest": true, "dateOfPublication": "2019-10-11T00:00:00+00:00", - "dateOfLastModification": "2025-09-09T17:41:50+00:00", + "dateOfLastModification": "2025-09-22T15:48:14+00:00", "categories": [ "Optimization" ], diff --git a/demonstrations_v2/tutorial_qcbm/metadata.json b/demonstrations_v2/tutorial_qcbm/metadata.json index 3af7384351..ad8e8cb83d 100644 --- a/demonstrations_v2/tutorial_qcbm/metadata.json +++ b/demonstrations_v2/tutorial_qcbm/metadata.json @@ -8,7 +8,7 @@ "executable_stable": true, "executable_latest": true, "dateOfPublication": "2024-05-22T00:00:00+00:00", - "dateOfLastModification": "2025-09-09T17:41:50+00:00", + "dateOfLastModification": "2025-09-22T15:48:14+00:00", "categories": [ "Quantum Machine Learning" ], diff --git a/demonstrations_v2/tutorial_qchem_external/demo.py b/demonstrations_v2/tutorial_qchem_external/demo.py index 2772f990b1..0e37c0aef2 100644 --- a/demonstrations_v2/tutorial_qchem_external/demo.py +++ b/demonstrations_v2/tutorial_qchem_external/demo.py @@ -17,7 +17,7 @@ The quantum chemistry module in PennyLane, :mod:`qchem `, provides built-in methods to compute molecular integrals, solve Hartree-Fock equations, and construct -`fully-differentiable `_ molecular +:doc:`fully-differentiable ` molecular Hamiltonians. PennyLane also lets you take advantage of various external resources and libraries to build upon existing tools. In this demo we will show you how to integrate PennyLane with `PySCF `_ and @@ -61,7 +61,7 @@ ############################################################################## # This generates a PennyLane :class:`~.pennylane.Hamiltonian` that can be used in a VQE workflow or # converted to a -# `sparse matrix `_ +# :doc:`sparse matrix ` # in the computational basis. # # Additionally, if you have built your electronic Hamiltonian independently using @@ -81,15 +81,15 @@ # Computing molecular integrals # ----------------------------- # In order to build a -# `molecular Hamiltonian `_, we need +# :doc:`molecular Hamiltonian `, we need # one- and two-electron integrals in the molecular orbital basis. These integrals are used to # construct a fermionic Hamiltonian which is then mapped onto the qubit basis. These molecular # integrals can be computed with the # :func:`~.pennylane.qchem.electron_integrals` function of PennyLane. Alternatively, the integrals # can be computed with the `PySCF `_ package and used in PennyLane # workflows such as building a -# `fermionic Hamiltonian `_ or -# quantum `resource estimation `_. +# :doc:`fermionic Hamiltonian ` or +# quantum :doc:`resource estimation `. # Let's use water as an example. # # First, we define the PySCF molecule object and run a restricted Hartree-Fock @@ -133,7 +133,7 @@ ############################################################################## # We now use the integrals to construct a fermionic Hamiltonian with PennyLane's powerful tools # for creating and manipulating -# `fermionic operators `_: +# :doc:`fermionic operators `: H_fermionic = qml.qchem.fermionic_observable(core_constant, one_mo, two_mo) diff --git a/demonstrations_v2/tutorial_qchem_external/metadata.json b/demonstrations_v2/tutorial_qchem_external/metadata.json index 5f374e12ea..9169b059e8 100644 --- a/demonstrations_v2/tutorial_qchem_external/metadata.json +++ b/demonstrations_v2/tutorial_qchem_external/metadata.json @@ -8,7 +8,7 @@ "executable_stable": true, "executable_latest": true, "dateOfPublication": "2023-01-03T00:00:00+00:00", - "dateOfLastModification": "2025-09-09T17:41:50+00:00", + "dateOfLastModification": "2025-09-22T15:48:14+00:00", "categories": [ "Quantum Chemistry", "Devices and Performance" diff --git a/demonstrations_v2/tutorial_qft/metadata.json b/demonstrations_v2/tutorial_qft/metadata.json index 836e771d21..7f945eba04 100644 --- a/demonstrations_v2/tutorial_qft/metadata.json +++ b/demonstrations_v2/tutorial_qft/metadata.json @@ -8,7 +8,7 @@ "executable_stable": true, "executable_latest": true, "dateOfPublication": "2024-04-16T00:00:00+00:00", - "dateOfLastModification": "2025-09-09T17:41:50+00:00", + "dateOfLastModification": "2025-09-22T15:48:14+00:00", "categories": [ "Algorithms", "Quantum Computing" diff --git a/demonstrations_v2/tutorial_qft_and_groups/metadata.json b/demonstrations_v2/tutorial_qft_and_groups/metadata.json index 21e331a01c..b018708fb9 100644 --- a/demonstrations_v2/tutorial_qft_and_groups/metadata.json +++ b/demonstrations_v2/tutorial_qft_and_groups/metadata.json @@ -8,7 +8,7 @@ "executable_stable": true, "executable_latest": true, "dateOfPublication": "2025-09-18T10:00:00+00:00", - "dateOfLastModification": "2025-09-18T10:00:00+00:01", + "dateOfLastModification": "2025-09-22T15:48:14+00:00", "categories": [ "Algorithms" ], @@ -78,7 +78,7 @@ "id": "tutorial_period_finding", "weight": 1.0 }, - { + { "type": "demonstration", "id": "tutorial_hidden_cut", "weight": 1.0 @@ -89,4 +89,4 @@ "weight": 1.0 } ] -} +} \ No newline at end of file diff --git a/demonstrations_v2/tutorial_qft_arithmetics/metadata.json b/demonstrations_v2/tutorial_qft_arithmetics/metadata.json index 8e0bdd5a53..12e31ee885 100644 --- a/demonstrations_v2/tutorial_qft_arithmetics/metadata.json +++ b/demonstrations_v2/tutorial_qft_arithmetics/metadata.json @@ -8,7 +8,7 @@ "executable_stable": true, "executable_latest": true, "dateOfPublication": "2022-11-07T00:00:00+00:00", - "dateOfLastModification": "2025-09-09T17:41:50+00:00", + "dateOfLastModification": "2025-09-22T15:48:14+00:00", "categories": [ "Getting Started", "Algorithms", diff --git a/demonstrations_v2/tutorial_qgrnn/metadata.json b/demonstrations_v2/tutorial_qgrnn/metadata.json index 40a6439b41..1d6f12a1f3 100644 --- a/demonstrations_v2/tutorial_qgrnn/metadata.json +++ b/demonstrations_v2/tutorial_qgrnn/metadata.json @@ -8,7 +8,7 @@ "executable_stable": true, "executable_latest": true, "dateOfPublication": "2020-07-27T00:00:00+00:00", - "dateOfLastModification": "2025-09-09T17:41:50+00:00", + "dateOfLastModification": "2025-09-22T15:48:14+00:00", "categories": [ "Quantum Machine Learning" ], diff --git a/demonstrations_v2/tutorial_qjit_compile_grovers_algorithm_with_catalyst/metadata.json b/demonstrations_v2/tutorial_qjit_compile_grovers_algorithm_with_catalyst/metadata.json index 553fa572bf..1a32682fc4 100644 --- a/demonstrations_v2/tutorial_qjit_compile_grovers_algorithm_with_catalyst/metadata.json +++ b/demonstrations_v2/tutorial_qjit_compile_grovers_algorithm_with_catalyst/metadata.json @@ -8,7 +8,7 @@ "executable_stable": true, "executable_latest": true, "dateOfPublication": "2024-11-07T00:00:00+00:00", - "dateOfLastModification": "2025-09-09T17:41:50+00:00", + "dateOfLastModification": "2025-09-22T15:48:14+00:00", "categories": [ "Algorithms", "Devices and Performance", diff --git a/demonstrations_v2/tutorial_qksd_qsp_qualtran/metadata.json b/demonstrations_v2/tutorial_qksd_qsp_qualtran/metadata.json index fb9bf96b1a..0863d83744 100644 --- a/demonstrations_v2/tutorial_qksd_qsp_qualtran/metadata.json +++ b/demonstrations_v2/tutorial_qksd_qsp_qualtran/metadata.json @@ -17,7 +17,7 @@ "executable_stable": true, "executable_latest": true, "dateOfPublication": "2025-08-29T00:00:00+00:00", - "dateOfLastModification": "2025-09-09T17:41:50+00:00", + "dateOfLastModification": "2025-09-22T15:48:14+00:00", "categories": [ "Quantum Computing" ], diff --git a/demonstrations_v2/tutorial_qnn_module_torch/metadata.json b/demonstrations_v2/tutorial_qnn_module_torch/metadata.json index 3abfc66a1b..7147ab3159 100644 --- a/demonstrations_v2/tutorial_qnn_module_torch/metadata.json +++ b/demonstrations_v2/tutorial_qnn_module_torch/metadata.json @@ -8,7 +8,7 @@ "executable_stable": true, "executable_latest": true, "dateOfPublication": "2020-11-02T00:00:00+00:00", - "dateOfLastModification": "2025-09-09T17:41:50+00:00", + "dateOfLastModification": "2025-09-22T15:48:14+00:00", "categories": [ "Devices and Performance", "Quantum Machine Learning" diff --git a/demonstrations_v2/tutorial_qnn_multivariate_regression/metadata.json b/demonstrations_v2/tutorial_qnn_multivariate_regression/metadata.json index 87b59e2bbe..b941aa03df 100644 --- a/demonstrations_v2/tutorial_qnn_multivariate_regression/metadata.json +++ b/demonstrations_v2/tutorial_qnn_multivariate_regression/metadata.json @@ -11,7 +11,7 @@ "executable_stable": true, "executable_latest": true, "dateOfPublication": "2024-10-01T00:00:00+00:00", - "dateOfLastModification": "2025-09-09T17:41:50+00:00", + "dateOfLastModification": "2025-09-22T15:48:14+00:00", "categories": [ "Quantum Computing", "Quantum Machine Learning", diff --git a/demonstrations_v2/tutorial_qpe/metadata.json b/demonstrations_v2/tutorial_qpe/metadata.json index af17c45219..2fb428705b 100644 --- a/demonstrations_v2/tutorial_qpe/metadata.json +++ b/demonstrations_v2/tutorial_qpe/metadata.json @@ -11,7 +11,7 @@ "executable_stable": true, "executable_latest": true, "dateOfPublication": "2024-01-30T00:00:00+00:00", - "dateOfLastModification": "2025-09-09T17:41:50+00:00", + "dateOfLastModification": "2025-09-22T15:48:14+00:00", "categories": [ "Algorithms", "Quantum Computing" diff --git a/demonstrations_v2/tutorial_qsvt_hardware/metadata.json b/demonstrations_v2/tutorial_qsvt_hardware/metadata.json index 4e9c687bf8..648534b0d9 100644 --- a/demonstrations_v2/tutorial_qsvt_hardware/metadata.json +++ b/demonstrations_v2/tutorial_qsvt_hardware/metadata.json @@ -8,7 +8,7 @@ "executable_stable": true, "executable_latest": true, "dateOfPublication": "2024-09-18T00:00:00+00:00", - "dateOfLastModification": "2025-09-09T17:41:50+00:00", + "dateOfLastModification": "2025-09-22T15:48:14+00:00", "categories": [ "Algorithms", "Quantum Computing", diff --git a/demonstrations_v2/tutorial_quantum_analytic_descent/demo.py b/demonstrations_v2/tutorial_quantum_analytic_descent/demo.py index f08508e341..a950327342 100644 --- a/demonstrations_v2/tutorial_quantum_analytic_descent/demo.py +++ b/demonstrations_v2/tutorial_quantum_analytic_descent/demo.py @@ -19,7 +19,7 @@ One of the main problems of many-body physics is that of finding the ground state and ground state energy of a given Hamiltonian. -`The Variational Quantum Eigensolver (VQE) `_ combines smart circuit +`The Variational Quantum Eigensolver (VQE) `_ combines smart circuit design with gradient-based optimization to solve this task. Several practical demonstrations have shown how near-term quantum devices may be suitable for VQE and other variational quantum algorithms. @@ -83,7 +83,7 @@ All parameters but :math:`\theta_i` are absorbed in the coefficients :math:`a_i,` :math:`b_i` and :math:`c_i.` Another technique using this structure of :math:`E(\boldsymbol{\theta})` are the -Rotosolve/Rotoselect algorithms [#Rotosolve]_ for which there also is `a PennyLane demo `__. +Rotosolve/Rotoselect algorithms [#Rotosolve]_ for which there also is `a PennyLane demo `__. Let's look at a toy example to illustrate this structure of the cost function. """ @@ -295,7 +295,7 @@ def circuit(parameters): # E^{(D)}_{kl} &= \frac{\partial^2 E(\boldsymbol{\theta})}{\partial\theta_k\partial\theta_l}\Bigg|_{\boldsymbol{\theta}=0} # # In PennyLane, computing the gradient of a cost function with respect to an array of parameters can be easily done -# with the `parameter-shift rule `_. +# with the `parameter-shift rule `_. # By iterating the rule, we can obtain the second derivatives – the Hessian (see for example [#higher_order_diff]_). # Let us implement a function that does just that and prepares the coefficients :math:`E^{(A/B/C/D)}:` diff --git a/demonstrations_v2/tutorial_quantum_analytic_descent/metadata.json b/demonstrations_v2/tutorial_quantum_analytic_descent/metadata.json index bf88fe747d..91f0768ded 100644 --- a/demonstrations_v2/tutorial_quantum_analytic_descent/metadata.json +++ b/demonstrations_v2/tutorial_quantum_analytic_descent/metadata.json @@ -11,7 +11,7 @@ "executable_stable": true, "executable_latest": true, "dateOfPublication": "2021-06-30T00:00:00+00:00", - "dateOfLastModification": "2025-09-09T17:41:50+00:00", + "dateOfLastModification": "2025-09-22T15:48:14+00:00", "categories": [ "Optimization" ], diff --git a/demonstrations_v2/tutorial_quantum_chebyshev_transform/metadata.json b/demonstrations_v2/tutorial_quantum_chebyshev_transform/metadata.json index 0eb3375f0f..9275e8cc09 100644 --- a/demonstrations_v2/tutorial_quantum_chebyshev_transform/metadata.json +++ b/demonstrations_v2/tutorial_quantum_chebyshev_transform/metadata.json @@ -8,7 +8,7 @@ "executable_stable": true, "executable_latest": true, "dateOfPublication": "2025-07-22T00:00:00+00:00", - "dateOfLastModification": "2025-09-09T17:41:50+00:00", + "dateOfLastModification": "2025-09-22T15:48:14+00:00", "categories": [ "Algorithms", "Quantum Machine Learning" diff --git a/demonstrations_v2/tutorial_quantum_chemistry/metadata.json b/demonstrations_v2/tutorial_quantum_chemistry/metadata.json index 67295b9e28..d39f7f407f 100644 --- a/demonstrations_v2/tutorial_quantum_chemistry/metadata.json +++ b/demonstrations_v2/tutorial_quantum_chemistry/metadata.json @@ -8,7 +8,7 @@ "executable_stable": true, "executable_latest": true, "dateOfPublication": "2020-08-02T00:00:00+00:00", - "dateOfLastModification": "2025-09-09T17:41:50+00:00", + "dateOfLastModification": "2025-09-22T15:48:14+00:00", "categories": [ "Quantum Chemistry" ], diff --git a/demonstrations_v2/tutorial_quantum_circuit_cutting/demo.py b/demonstrations_v2/tutorial_quantum_circuit_cutting/demo.py index af0aa39ea9..92019fe244 100644 --- a/demonstrations_v2/tutorial_quantum_circuit_cutting/demo.py +++ b/demonstrations_v2/tutorial_quantum_circuit_cutting/demo.py @@ -334,7 +334,7 @@ def circuit(x): # collection of unitaries such that the average of any degree 2 polynomial # function of a linear operator over the design is exactly the same as the # average over Haar random measure. For further explanation of this measure read -# the `Haar Measure demo `__. +# the :doc:`Haar Measure demo `. # # More precisely, let :math:`P(U)` be a polynomial with homogeneous degree at most two in # the entries of a unitary matrix :math:`U,` and degree two in the complex @@ -345,8 +345,7 @@ def circuit(x): # # The elements of the Clifford group over the qubits being cut are an # example of a 2-design. We don’t have a lot of space here to go into too -# many details. But fear not - there is an `entire -# demo `__ +# many details. But fear not - there is an :doc:`entire demo ` # dedicated to this wonderful topic! # # .. figure:: ../_static/demonstration_assets/quantum_circuit_cutting/flowchart.svg @@ -405,8 +404,7 @@ def circuit(x): # We have seen that looking at circuit cutting through the lens of # 2-designs can be a source of considerable speedups. A good test case # where one may care about accurately estimating an observable is the -# `Quantum Approximate Optimization -# Algorithm `__ +# :doc:`Quantum Approximate Optimization Algorithm ` # (QAOA). In its simplest form, QAOA concerns itself with finding a # lowest energy state of a *cost Hamiltonian* :math:`H_{\mathcal{C}}:` # @@ -841,8 +839,7 @@ def make_kraus_ops(num_wires: int): # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ # # Careful readers may have noticed that QAOA at depth :math:`p=1` has a -# specific structure of a `Matrix Product -# State `__ +# specific structure of a :doc:`Matrix Product State ` # (MPS) circuit. However, in order to cut a :math:`p=2` QAOA circuit, we # would need 2 cuts. This introduces some subtleties within the context of # classical simulation that we point out here. diff --git a/demonstrations_v2/tutorial_quantum_circuit_cutting/metadata.json b/demonstrations_v2/tutorial_quantum_circuit_cutting/metadata.json index 17ec6b8fb9..348f39c08e 100644 --- a/demonstrations_v2/tutorial_quantum_circuit_cutting/metadata.json +++ b/demonstrations_v2/tutorial_quantum_circuit_cutting/metadata.json @@ -14,7 +14,7 @@ "executable_stable": true, "executable_latest": true, "dateOfPublication": "2022-09-02T00:00:00+00:00", - "dateOfLastModification": "2025-09-09T17:41:50+00:00", + "dateOfLastModification": "2025-09-22T15:48:14+00:00", "categories": [ "Algorithms", "Quantum Computing" diff --git a/demonstrations_v2/tutorial_quantum_dropout/metadata.json b/demonstrations_v2/tutorial_quantum_dropout/metadata.json index 3ccdafc1b3..3620227380 100644 --- a/demonstrations_v2/tutorial_quantum_dropout/metadata.json +++ b/demonstrations_v2/tutorial_quantum_dropout/metadata.json @@ -8,7 +8,7 @@ "executable_stable": true, "executable_latest": true, "dateOfPublication": "2024-03-12T00:00:00+00:00", - "dateOfLastModification": "2025-09-09T17:41:50+00:00", + "dateOfLastModification": "2025-09-22T15:48:14+00:00", "categories": [ "Quantum Machine Learning" ], diff --git a/demonstrations_v2/tutorial_quantum_gans/metadata.json b/demonstrations_v2/tutorial_quantum_gans/metadata.json index 4a137e88f9..c07e0c8b38 100644 --- a/demonstrations_v2/tutorial_quantum_gans/metadata.json +++ b/demonstrations_v2/tutorial_quantum_gans/metadata.json @@ -8,7 +8,7 @@ "executable_stable": true, "executable_latest": true, "dateOfPublication": "2022-02-01T00:00:00+00:00", - "dateOfLastModification": "2025-09-09T17:41:50+00:00", + "dateOfLastModification": "2025-09-22T15:48:14+00:00", "categories": [ "Quantum Machine Learning" ], diff --git a/demonstrations_v2/tutorial_quantum_metrology/metadata.json b/demonstrations_v2/tutorial_quantum_metrology/metadata.json index 17f914b408..fe41d7d894 100644 --- a/demonstrations_v2/tutorial_quantum_metrology/metadata.json +++ b/demonstrations_v2/tutorial_quantum_metrology/metadata.json @@ -8,7 +8,7 @@ "executable_stable": true, "executable_latest": true, "dateOfPublication": "2020-06-18T00:00:00+00:00", - "dateOfLastModification": "2025-09-09T17:41:50+00:00", + "dateOfLastModification": "2025-09-22T15:48:14+00:00", "categories": [ "Optimization" ], diff --git a/demonstrations_v2/tutorial_quantum_natural_gradient/demo.py b/demonstrations_v2/tutorial_quantum_natural_gradient/demo.py index d502b6b949..44f59b57e6 100644 --- a/demonstrations_v2/tutorial_quantum_natural_gradient/demo.py +++ b/demonstrations_v2/tutorial_quantum_natural_gradient/demo.py @@ -31,7 +31,7 @@ of the quantum device. Examples of such algorithms include the :doc:`variational quantum eigensolver (VQE) `, the `quantum approximate optimization algorithm (QAOA) `__, -and :ref:`quantum neural networks (QNN) `. +and :doc:`quantum neural networks (QNN) `. Most recent demonstrations of variational quantum algorithms have used gradient-free classical optimization diff --git a/demonstrations_v2/tutorial_quantum_natural_gradient/metadata.json b/demonstrations_v2/tutorial_quantum_natural_gradient/metadata.json index 3c2567b99a..3c62496128 100644 --- a/demonstrations_v2/tutorial_quantum_natural_gradient/metadata.json +++ b/demonstrations_v2/tutorial_quantum_natural_gradient/metadata.json @@ -8,7 +8,7 @@ "executable_stable": true, "executable_latest": true, "dateOfPublication": "2019-10-11T00:00:00+00:00", - "dateOfLastModification": "2025-09-09T17:41:50+00:00", + "dateOfLastModification": "2025-09-22T15:48:14+00:00", "categories": [ "Optimization" ], diff --git a/demonstrations_v2/tutorial_quantum_transfer_learning/metadata.json b/demonstrations_v2/tutorial_quantum_transfer_learning/metadata.json index 016ecf724b..17da3abb02 100644 --- a/demonstrations_v2/tutorial_quantum_transfer_learning/metadata.json +++ b/demonstrations_v2/tutorial_quantum_transfer_learning/metadata.json @@ -8,7 +8,7 @@ "executable_stable": true, "executable_latest": true, "dateOfPublication": "2019-12-19T00:00:00+00:00", - "dateOfLastModification": "2025-09-09T17:41:50+00:00", + "dateOfLastModification": "2025-09-22T15:48:14+00:00", "categories": [ "Quantum Machine Learning" ], diff --git a/demonstrations_v2/tutorial_quanvolution/metadata.json b/demonstrations_v2/tutorial_quanvolution/metadata.json index b3c95c3e64..eedcfc0208 100644 --- a/demonstrations_v2/tutorial_quanvolution/metadata.json +++ b/demonstrations_v2/tutorial_quanvolution/metadata.json @@ -8,7 +8,7 @@ "executable_stable": true, "executable_latest": true, "dateOfPublication": "2020-03-24T00:00:00+00:00", - "dateOfLastModification": "2025-09-09T17:41:50+00:00", + "dateOfLastModification": "2025-09-22T15:48:14+00:00", "categories": [ "Quantum Machine Learning" ], diff --git a/demonstrations_v2/tutorial_qubit_rotation/demo.py b/demonstrations_v2/tutorial_qubit_rotation/demo.py index 4a9eb57dd6..13e3600a47 100644 --- a/demonstrations_v2/tutorial_qubit_rotation/demo.py +++ b/demonstrations_v2/tutorial_qubit_rotation/demo.py @@ -362,6 +362,6 @@ def cost(x): # internal hyperparameters that are stored in the optimizer instance. These can # be reset using the :meth:`reset` method. # -# Continue on to the next tutorial, :ref:`gaussian_transformation`, to see a similar example using +# Continue on to the next tutorial, :doc:`gaussian transformation `, to see a similar example using # continuous-variable (CV) quantum nodes. # diff --git a/demonstrations_v2/tutorial_qubit_rotation/metadata.json b/demonstrations_v2/tutorial_qubit_rotation/metadata.json index a084926a65..93f2361517 100644 --- a/demonstrations_v2/tutorial_qubit_rotation/metadata.json +++ b/demonstrations_v2/tutorial_qubit_rotation/metadata.json @@ -8,7 +8,7 @@ "executable_stable": true, "executable_latest": true, "dateOfPublication": "2019-10-11T00:00:00+00:00", - "dateOfLastModification": "2025-09-09T17:41:50+00:00", + "dateOfLastModification": "2025-09-22T15:48:14+00:00", "categories": [ "Getting Started" ], diff --git a/demonstrations_v2/tutorial_qubit_tapering/demo.py b/demonstrations_v2/tutorial_qubit_tapering/demo.py index c6d97c45f5..5f338e68f3 100644 --- a/demonstrations_v2/tutorial_qubit_tapering/demo.py +++ b/demonstrations_v2/tutorial_qubit_tapering/demo.py @@ -241,7 +241,7 @@ def circuit(): # -------------- # Finally, we can use the tapered Hamiltonian and the tapered reference state to perform a VQE # simulation and compute the ground-state energy of the :math:`\textrm{HeH}^+` cation. We build a -# tapered variational ansatz `[3] `__ +# tapered variational ansatz :doc:`[3] ` # that prepares an entangled state by evolving the tapered Hartree-Fock state using the tapered # particle-conserving gates, i.e., the :func:`~.pennylane.SingleExcitation` and # :func:`~.pennylane.DoubleExcitation` operations tapered using diff --git a/demonstrations_v2/tutorial_qubit_tapering/metadata.json b/demonstrations_v2/tutorial_qubit_tapering/metadata.json index 399cfc1e41..42212516f7 100644 --- a/demonstrations_v2/tutorial_qubit_tapering/metadata.json +++ b/demonstrations_v2/tutorial_qubit_tapering/metadata.json @@ -11,7 +11,7 @@ "executable_stable": true, "executable_latest": true, "dateOfPublication": "2022-05-16T00:00:00+00:00", - "dateOfLastModification": "2025-09-09T17:41:50+00:00", + "dateOfLastModification": "2025-09-22T15:48:14+00:00", "categories": [ "Quantum Chemistry" ], diff --git a/demonstrations_v2/tutorial_qubitization/metadata.json b/demonstrations_v2/tutorial_qubitization/metadata.json index f23a146b65..7df408a5e5 100644 --- a/demonstrations_v2/tutorial_qubitization/metadata.json +++ b/demonstrations_v2/tutorial_qubitization/metadata.json @@ -11,7 +11,7 @@ "executable_stable": true, "executable_latest": true, "dateOfPublication": "2024-09-09T00:00:00+00:00", - "dateOfLastModification": "2025-09-09T17:41:50+00:00", + "dateOfLastModification": "2025-09-22T15:48:14+00:00", "categories": [ "Quantum Computing", "Algorithms" diff --git a/demonstrations_v2/tutorial_qutrits_bernstein_vazirani/metadata.json b/demonstrations_v2/tutorial_qutrits_bernstein_vazirani/metadata.json index bb4371beb0..d2a7843400 100644 --- a/demonstrations_v2/tutorial_qutrits_bernstein_vazirani/metadata.json +++ b/demonstrations_v2/tutorial_qutrits_bernstein_vazirani/metadata.json @@ -8,7 +8,7 @@ "executable_stable": true, "executable_latest": true, "dateOfPublication": "2023-05-09T00:00:00+00:00", - "dateOfLastModification": "2025-09-09T17:41:50+00:00", + "dateOfLastModification": "2025-09-22T15:48:14+00:00", "categories": [ "Algorithms", "Quantum Computing" diff --git a/demonstrations_v2/tutorial_resource_estimation/demo.py b/demonstrations_v2/tutorial_resource_estimation/demo.py index a45325f96b..7c60ff3e3d 100644 --- a/demonstrations_v2/tutorial_resource_estimation/demo.py +++ b/demonstrations_v2/tutorial_resource_estimation/demo.py @@ -15,7 +15,7 @@ Quantum algorithms such as `quantum phase estimation `_ -(QPE) and the `variational quantum eigensolver `_ (VQE) +(QPE) and the `variational quantum eigensolver `_ (VQE) are widely studied in quantum chemistry as potential avenues to tackle problems that are intractable for conventional computers. However, we currently do not have quantum computers or simulators capable of implementing large-scale @@ -56,7 +56,7 @@ We study the double low-rank Hamiltonian factorization algorithm of [#vonburg2021]_ and use its cost equations as provided in APPENDIX C of [#lee2021]_. This algorithm requires the one- and two-electron -`integrals `_ +`integrals `_ as input. These integrals can be obtained in different ways and here we use PennyLane to compute them. We first need to define the atomic symbols and coordinates for the given molecule. Let's use the water molecule at its equilibrium geometry with the diff --git a/demonstrations_v2/tutorial_resource_estimation/metadata.json b/demonstrations_v2/tutorial_resource_estimation/metadata.json index 28e59e38dc..13d916651f 100644 --- a/demonstrations_v2/tutorial_resource_estimation/metadata.json +++ b/demonstrations_v2/tutorial_resource_estimation/metadata.json @@ -8,7 +8,7 @@ "executable_stable": true, "executable_latest": true, "dateOfPublication": "2022-11-21T00:00:00+00:00", - "dateOfLastModification": "2025-09-09T17:41:50+00:00", + "dateOfLastModification": "2025-09-22T15:48:14+00:00", "categories": [ "Quantum Chemistry" ], diff --git a/demonstrations_v2/tutorial_rl_pulse/metadata.json b/demonstrations_v2/tutorial_rl_pulse/metadata.json index 7a1b15e719..a0dbaaf21f 100644 --- a/demonstrations_v2/tutorial_rl_pulse/metadata.json +++ b/demonstrations_v2/tutorial_rl_pulse/metadata.json @@ -8,7 +8,7 @@ "executable_stable": true, "executable_latest": true, "dateOfPublication": "2024-04-09T00:00:00+00:00", - "dateOfLastModification": "2025-09-09T17:41:50+00:00", + "dateOfLastModification": "2025-09-22T15:48:14+00:00", "categories": [ "Getting Started", "Optimization", diff --git a/demonstrations_v2/tutorial_rosalin/metadata.json b/demonstrations_v2/tutorial_rosalin/metadata.json index d3bf6deff6..262a4c13f7 100644 --- a/demonstrations_v2/tutorial_rosalin/metadata.json +++ b/demonstrations_v2/tutorial_rosalin/metadata.json @@ -8,7 +8,7 @@ "executable_stable": true, "executable_latest": true, "dateOfPublication": "2020-05-19T00:00:00+00:00", - "dateOfLastModification": "2025-09-09T17:41:50+00:00", + "dateOfLastModification": "2025-09-22T15:48:14+00:00", "categories": [ "Optimization" ], diff --git a/demonstrations_v2/tutorial_rotoselect/metadata.json b/demonstrations_v2/tutorial_rotoselect/metadata.json index 1fcc9792c0..43f3120047 100644 --- a/demonstrations_v2/tutorial_rotoselect/metadata.json +++ b/demonstrations_v2/tutorial_rotoselect/metadata.json @@ -8,7 +8,7 @@ "executable_stable": true, "executable_latest": true, "dateOfPublication": "2019-10-16T00:00:00+00:00", - "dateOfLastModification": "2025-09-09T17:41:50+00:00", + "dateOfLastModification": "2025-09-22T15:48:14+00:00", "categories": [ "Optimization" ], diff --git a/demonstrations_v2/tutorial_sc_qubits/metadata.json b/demonstrations_v2/tutorial_sc_qubits/metadata.json index df80ad838e..48a93bc253 100644 --- a/demonstrations_v2/tutorial_sc_qubits/metadata.json +++ b/demonstrations_v2/tutorial_sc_qubits/metadata.json @@ -8,7 +8,7 @@ "executable_stable": true, "executable_latest": true, "dateOfPublication": "2022-03-22T00:00:00+00:00", - "dateOfLastModification": "2025-09-22T17:41:50+00:00", + "dateOfLastModification": "2025-09-22T15:48:14+00:00", "categories": [ "Quantum Hardware", "Quantum Computing" diff --git a/demonstrations_v2/tutorial_shadow_hamiltonian_simulation/metadata.json b/demonstrations_v2/tutorial_shadow_hamiltonian_simulation/metadata.json index a0f85fc06a..17ef1b5b3d 100644 --- a/demonstrations_v2/tutorial_shadow_hamiltonian_simulation/metadata.json +++ b/demonstrations_v2/tutorial_shadow_hamiltonian_simulation/metadata.json @@ -8,7 +8,7 @@ "executable_stable": true, "executable_latest": true, "dateOfPublication": "2024-08-06T00:00:00+00:00", - "dateOfLastModification": "2025-09-09T17:41:50+00:00", + "dateOfLastModification": "2025-09-22T15:48:14+00:00", "categories": [ "Quantum Computing", "Algorithms" diff --git a/demonstrations_v2/tutorial_shors_algorithm_catalyst/metadata.json b/demonstrations_v2/tutorial_shors_algorithm_catalyst/metadata.json index a6092c7f69..4382a15831 100644 --- a/demonstrations_v2/tutorial_shors_algorithm_catalyst/metadata.json +++ b/demonstrations_v2/tutorial_shors_algorithm_catalyst/metadata.json @@ -8,7 +8,7 @@ "executable_stable": true, "executable_latest": true, "dateOfPublication": "2025-04-04T09:00:00+00:00", - "dateOfLastModification": "2025-09-09T17:41:50+00:00", + "dateOfLastModification": "2025-09-22T15:48:14+00:00", "categories": [ "Algorithms", "Quantum Computing", diff --git a/demonstrations_v2/tutorial_spsa/metadata.json b/demonstrations_v2/tutorial_spsa/metadata.json index 13bcb48892..05dad3897c 100644 --- a/demonstrations_v2/tutorial_spsa/metadata.json +++ b/demonstrations_v2/tutorial_spsa/metadata.json @@ -11,7 +11,7 @@ "executable_stable": true, "executable_latest": true, "dateOfPublication": "2023-03-19T00:00:00+00:00", - "dateOfLastModification": "2025-09-09T17:41:50+00:00", + "dateOfLastModification": "2025-09-22T15:48:14+00:00", "categories": [ "Optimization" ], diff --git a/demonstrations_v2/tutorial_stabilizer_codes/metadata.json b/demonstrations_v2/tutorial_stabilizer_codes/metadata.json index 43d7de1c61..b8f6505676 100644 --- a/demonstrations_v2/tutorial_stabilizer_codes/metadata.json +++ b/demonstrations_v2/tutorial_stabilizer_codes/metadata.json @@ -11,7 +11,7 @@ "executable_stable": true, "executable_latest": true, "dateOfPublication": "2025-08-19T09:00:00+00:00", - "dateOfLastModification": "2025-09-09T17:41:50+00:00", + "dateOfLastModification": "2025-09-22T15:48:14+00:00", "categories": [ "Algorithms", "Quantum Computing", diff --git a/demonstrations_v2/tutorial_state_preparation/metadata.json b/demonstrations_v2/tutorial_state_preparation/metadata.json index 104af6d759..61dfbdd860 100644 --- a/demonstrations_v2/tutorial_state_preparation/metadata.json +++ b/demonstrations_v2/tutorial_state_preparation/metadata.json @@ -8,7 +8,7 @@ "executable_stable": true, "executable_latest": true, "dateOfPublication": "2019-10-11T00:00:00+00:00", - "dateOfLastModification": "2025-09-09T17:41:50+00:00", + "dateOfLastModification": "2025-09-22T15:48:14+00:00", "categories": [ "Devices and Performance" ], diff --git a/demonstrations_v2/tutorial_stochastic_parameter_shift/metadata.json b/demonstrations_v2/tutorial_stochastic_parameter_shift/metadata.json index 72941b051c..a750024a76 100644 --- a/demonstrations_v2/tutorial_stochastic_parameter_shift/metadata.json +++ b/demonstrations_v2/tutorial_stochastic_parameter_shift/metadata.json @@ -8,7 +8,7 @@ "executable_stable": true, "executable_latest": true, "dateOfPublication": "2020-05-25T00:00:00+00:00", - "dateOfLastModification": "2025-09-09T17:41:50+00:00", + "dateOfLastModification": "2025-09-22T15:48:14+00:00", "categories": [ "Optimization" ], diff --git a/demonstrations_v2/tutorial_teleportation/metadata.json b/demonstrations_v2/tutorial_teleportation/metadata.json index 72695cccfd..cfc4b7f9bd 100644 --- a/demonstrations_v2/tutorial_teleportation/metadata.json +++ b/demonstrations_v2/tutorial_teleportation/metadata.json @@ -8,7 +8,7 @@ "executable_stable": true, "executable_latest": true, "dateOfPublication": "2023-10-20T00:00:00+00:00", - "dateOfLastModification": "2025-09-09T17:41:50+00:00", + "dateOfLastModification": "2025-09-22T15:48:14+00:00", "categories": [ "Getting Started", "Quantum Computing" diff --git a/demonstrations_v2/tutorial_tensor_network_basics/metadata.json b/demonstrations_v2/tutorial_tensor_network_basics/metadata.json index a9aa133798..eb1aad94fd 100644 --- a/demonstrations_v2/tutorial_tensor_network_basics/metadata.json +++ b/demonstrations_v2/tutorial_tensor_network_basics/metadata.json @@ -8,7 +8,7 @@ "executable_stable": true, "executable_latest": true, "dateOfPublication": "2025-01-23T09:00:00+00:00", - "dateOfLastModification": "2025-09-09T17:41:50+00:00", + "dateOfLastModification": "2025-09-22T15:48:14+00:00", "categories": [ "Getting Started", "Quantum Computing", diff --git a/demonstrations_v2/tutorial_testing_symmetry/demo.py b/demonstrations_v2/tutorial_testing_symmetry/demo.py index 192a31de37..bc8963d699 100644 --- a/demonstrations_v2/tutorial_testing_symmetry/demo.py +++ b/demonstrations_v2/tutorial_testing_symmetry/demo.py @@ -53,7 +53,7 @@ U(g_1)U(g_2) = U(g_1 \circ g_2). -For more on groups and how to represent them with matrices, see our `demo on geometric learning `__. +For more on groups and how to represent them with matrices, see our `demo on geometric learning `__. For the Hamiltonian to respect the symmetries encoded in the group :math:`G` it means that it commutes with the matrices, .. math:: diff --git a/demonstrations_v2/tutorial_testing_symmetry/metadata.json b/demonstrations_v2/tutorial_testing_symmetry/metadata.json index e4868a09f4..688ed12a2a 100644 --- a/demonstrations_v2/tutorial_testing_symmetry/metadata.json +++ b/demonstrations_v2/tutorial_testing_symmetry/metadata.json @@ -8,7 +8,7 @@ "executable_stable": true, "executable_latest": true, "dateOfPublication": "2023-01-24T00:00:00+00:00", - "dateOfLastModification": "2025-09-09T17:41:50+00:00", + "dateOfLastModification": "2025-09-22T15:48:14+00:00", "categories": [ "Algorithms", "Quantum Computing" diff --git a/demonstrations_v2/tutorial_tn_circuits/metadata.json b/demonstrations_v2/tutorial_tn_circuits/metadata.json index d2060d03d0..bfb7e23fd6 100644 --- a/demonstrations_v2/tutorial_tn_circuits/metadata.json +++ b/demonstrations_v2/tutorial_tn_circuits/metadata.json @@ -17,7 +17,7 @@ "executable_stable": true, "executable_latest": true, "dateOfPublication": "2022-03-29T00:00:00+00:00", - "dateOfLastModification": "2025-09-09T17:41:50+00:00", + "dateOfLastModification": "2025-09-22T15:48:14+00:00", "categories": [ "Quantum Machine Learning" ], diff --git a/demonstrations_v2/tutorial_toric_code/metadata.json b/demonstrations_v2/tutorial_toric_code/metadata.json index d4ceaf9cd5..96780d5ac6 100644 --- a/demonstrations_v2/tutorial_toric_code/metadata.json +++ b/demonstrations_v2/tutorial_toric_code/metadata.json @@ -8,7 +8,7 @@ "executable_stable": true, "executable_latest": true, "dateOfPublication": "2022-08-08T00:00:00+00:00", - "dateOfLastModification": "2025-09-09T17:41:50+00:00", + "dateOfLastModification": "2025-09-22T15:48:14+00:00", "categories": [ "Algorithms", "Quantum Computing" diff --git a/demonstrations_v2/tutorial_trapped_ions/metadata.json b/demonstrations_v2/tutorial_trapped_ions/metadata.json index a2a9eb23f2..97640967e7 100644 --- a/demonstrations_v2/tutorial_trapped_ions/metadata.json +++ b/demonstrations_v2/tutorial_trapped_ions/metadata.json @@ -8,7 +8,7 @@ "executable_stable": true, "executable_latest": true, "dateOfPublication": "2021-11-10T00:00:00+00:00", - "dateOfLastModification": "2025-09-09T17:41:50+00:00", + "dateOfLastModification": "2025-09-22T15:48:14+00:00", "categories": [ "Quantum Hardware", "Quantum Computing" diff --git a/demonstrations_v2/tutorial_unitary_designs/metadata.json b/demonstrations_v2/tutorial_unitary_designs/metadata.json index bb01e3bbc5..f0e508788b 100644 --- a/demonstrations_v2/tutorial_unitary_designs/metadata.json +++ b/demonstrations_v2/tutorial_unitary_designs/metadata.json @@ -8,7 +8,7 @@ "executable_stable": true, "executable_latest": true, "dateOfPublication": "2021-09-07T00:00:00+00:00", - "dateOfLastModification": "2024-10-11T00:00:00+00:00", + "dateOfLastModification": "2025-09-22T15:48:14+00:00", "categories": [ "Quantum Machine Learning", "Quantum Computing" @@ -160,4 +160,4 @@ "weight": 1.0 } ] -} +} \ No newline at end of file diff --git a/demonstrations_v2/tutorial_unitary_synthesis_kak/metadata.json b/demonstrations_v2/tutorial_unitary_synthesis_kak/metadata.json index 4f85f11e3e..12703a8bb9 100644 --- a/demonstrations_v2/tutorial_unitary_synthesis_kak/metadata.json +++ b/demonstrations_v2/tutorial_unitary_synthesis_kak/metadata.json @@ -8,7 +8,7 @@ "executable_stable": true, "executable_latest": true, "dateOfPublication": "2025-05-30T09:00:00+00:00", - "dateOfLastModification": "2025-09-09T17:41:50+00:00", + "dateOfLastModification": "2025-09-22T15:48:14+00:00", "categories": [ "Quantum Computing" ], diff --git a/demonstrations_v2/tutorial_univariate_qvr/metadata.json b/demonstrations_v2/tutorial_univariate_qvr/metadata.json index 0905c65d77..cbdc56b885 100644 --- a/demonstrations_v2/tutorial_univariate_qvr/metadata.json +++ b/demonstrations_v2/tutorial_univariate_qvr/metadata.json @@ -11,7 +11,7 @@ "executable_stable": true, "executable_latest": true, "dateOfPublication": "2023-02-07T00:00:00+00:00", - "dateOfLastModification": "2025-09-09T17:41:50+00:00", + "dateOfLastModification": "2025-09-22T15:48:14+00:00", "categories": [ "Quantum Machine Learning" ], diff --git a/demonstrations_v2/tutorial_variational_classifier/metadata.json b/demonstrations_v2/tutorial_variational_classifier/metadata.json index bed3ee4462..12394ad6ad 100644 --- a/demonstrations_v2/tutorial_variational_classifier/metadata.json +++ b/demonstrations_v2/tutorial_variational_classifier/metadata.json @@ -8,7 +8,7 @@ "executable_stable": true, "executable_latest": true, "dateOfPublication": "2019-10-11T00:00:00+00:00", - "dateOfLastModification": "2025-09-09T17:41:50+00:00", + "dateOfLastModification": "2025-09-22T15:48:14+00:00", "categories": [ "Quantum Machine Learning", "Getting Started" diff --git a/demonstrations_v2/tutorial_vqe/metadata.json b/demonstrations_v2/tutorial_vqe/metadata.json index 5905c52a10..72bbf9d642 100644 --- a/demonstrations_v2/tutorial_vqe/metadata.json +++ b/demonstrations_v2/tutorial_vqe/metadata.json @@ -8,7 +8,7 @@ "executable_stable": true, "executable_latest": true, "dateOfPublication": "2020-02-08T00:00:00+00:00", - "dateOfLastModification": "2025-09-09T17:41:50+00:00", + "dateOfLastModification": "2025-09-22T15:48:14+00:00", "categories": [ "Quantum Chemistry", "Getting Started" diff --git a/demonstrations_v2/tutorial_vqe_qng/demo.py b/demonstrations_v2/tutorial_vqe_qng/demo.py index 685357706d..d235c5fbc9 100644 --- a/demonstrations_v2/tutorial_vqe_qng/demo.py +++ b/demonstrations_v2/tutorial_vqe_qng/demo.py @@ -268,7 +268,7 @@ def cost_fn(params): ############################################################################## # For our ansatz, we use the circuit from the -# `VQE tutorial `__ +# `VQE tutorial `__ # but expand out the arbitrary single-qubit rotations to elementary # gates (RZ-RY-RZ). diff --git a/demonstrations_v2/tutorial_vqe_qng/metadata.json b/demonstrations_v2/tutorial_vqe_qng/metadata.json index 94a6b3fa1b..5a27a98566 100644 --- a/demonstrations_v2/tutorial_vqe_qng/metadata.json +++ b/demonstrations_v2/tutorial_vqe_qng/metadata.json @@ -14,7 +14,7 @@ "executable_stable": true, "executable_latest": true, "dateOfPublication": "2020-11-06T00:00:00+00:00", - "dateOfLastModification": "2025-09-09T17:41:50+00:00", + "dateOfLastModification": "2025-09-22T15:48:14+00:00", "categories": [ "Optimization" ], diff --git a/demonstrations_v2/tutorial_vqe_spin_sectors/metadata.json b/demonstrations_v2/tutorial_vqe_spin_sectors/metadata.json index 6206ebd3f9..c7c38851ea 100644 --- a/demonstrations_v2/tutorial_vqe_spin_sectors/metadata.json +++ b/demonstrations_v2/tutorial_vqe_spin_sectors/metadata.json @@ -8,7 +8,7 @@ "executable_stable": true, "executable_latest": true, "dateOfPublication": "2020-10-13T00:00:00+00:00", - "dateOfLastModification": "2025-09-09T17:41:50+00:00", + "dateOfLastModification": "2025-09-22T15:48:14+00:00", "categories": [ "Quantum Chemistry" ], diff --git a/demonstrations_v2/tutorial_vqe_vqd/demo.py b/demonstrations_v2/tutorial_vqe_vqd/demo.py index 091e098d85..16df58dece 100644 --- a/demonstrations_v2/tutorial_vqe_vqd/demo.py +++ b/demonstrations_v2/tutorial_vqe_vqd/demo.py @@ -5,7 +5,7 @@ eigenvalue, but sometimes we are interested in other eigenvalues. Here we will show you how to implement the variational quantum deflation (VQD) algorithm in PennyLane and find the first excited state energy of the `hydrogen molecule `__. To benefit the most from this tutorial, we recommend -a familiarization with the `variational quantum eigensolver (VQE) algorithm `__ first. +a familiarization with the `variational quantum eigensolver (VQE) algorithm `__ first. .. figure:: ../_static/demo_thumbnails/opengraph_demo_thumbnails/OGthumbnail_how_to_vqd_pennylane.png :align: center @@ -20,7 +20,7 @@ # ------------------------------ # # The VQD algorithm [#Vqd]_ is a method used to find the excited states of a quantum system. -# It is related to the `VQE algorithm `__, which is often used to find the ground state energy of a quantum system. +# It is related to the `VQE algorithm `__, which is often used to find the ground state energy of a quantum system. # The main idea of the VQE algorithm is to define a quantum state ansatz that depends on adjustable parameters :math:`\theta` and minimize the energy of the system, computed as: # # .. math:: C_0(\theta) = \left\langle\Psi_0 (\theta)|\hat H |\Psi_0 (\theta) \right\rangle, diff --git a/demonstrations_v2/tutorial_vqe_vqd/metadata.json b/demonstrations_v2/tutorial_vqe_vqd/metadata.json index 1826c67d13..d22fd0a441 100644 --- a/demonstrations_v2/tutorial_vqe_vqd/metadata.json +++ b/demonstrations_v2/tutorial_vqe_vqd/metadata.json @@ -11,7 +11,7 @@ "executable_stable": true, "executable_latest": true, "dateOfPublication": "2024-08-26T00:00:00+00:00", - "dateOfLastModification": "2025-09-09T17:41:50+00:00", + "dateOfLastModification": "2025-09-22T15:48:14+00:00", "categories": [ "Quantum Chemistry", "How-to" diff --git a/demonstrations_v2/tutorial_vqls/metadata.json b/demonstrations_v2/tutorial_vqls/metadata.json index 92c2e67f20..7040bfb5df 100644 --- a/demonstrations_v2/tutorial_vqls/metadata.json +++ b/demonstrations_v2/tutorial_vqls/metadata.json @@ -8,7 +8,7 @@ "executable_stable": true, "executable_latest": true, "dateOfPublication": "2019-11-04T00:00:00+00:00", - "dateOfLastModification": "2025-09-09T17:41:50+00:00", + "dateOfLastModification": "2025-09-22T15:48:14+00:00", "categories": [ "Optimization" ], diff --git a/demonstrations_v2/tutorial_vqt/metadata.json b/demonstrations_v2/tutorial_vqt/metadata.json index 07afbca777..f1aa07f6c3 100644 --- a/demonstrations_v2/tutorial_vqt/metadata.json +++ b/demonstrations_v2/tutorial_vqt/metadata.json @@ -8,7 +8,7 @@ "executable_stable": true, "executable_latest": true, "dateOfPublication": "2020-07-07T00:00:00+00:00", - "dateOfLastModification": "2025-09-09T17:41:50+00:00", + "dateOfLastModification": "2025-09-22T15:48:14+00:00", "categories": [ "Optimization" ], diff --git a/demonstrations_v2/tutorial_xas/demo.py b/demonstrations_v2/tutorial_xas/demo.py index d65a024969..17c35b1dd1 100644 --- a/demonstrations_v2/tutorial_xas/demo.py +++ b/demonstrations_v2/tutorial_xas/demo.py @@ -15,7 +15,7 @@ .. admonition:: Prerequisite understanding :class: note - We will be using concepts that were introduced in other PennyLane demos, such as `Using PennyLane with PySCF and OpenFermion `__, `Initial state preparation for quantum chemistry `__, and `How to build compressed double-factorized Hamiltonians `__. + We will be using concepts that were introduced in other PennyLane demos, such as :doc:`Using PennyLane with PySCF and OpenFermion `, :doc:`Initial state preparation for quantum chemistry `, and :doc:`How to build compressed double-factorized Hamiltonians `. If you haven’t checked out those demos yet, it might be best to do so and then come back here 🔙. .. figure:: ../_static/demo_thumbnails/opengraph_demo_thumbnails/OGthumbnail_xas.png @@ -143,7 +143,7 @@ Ground state calculation ~~~~~~~~~~~~~~~~~~~~~~~~ -If you haven’t, check out the demo `“Initial state preparation for quantum chemistry” `__. +If you haven’t, check out the demo `“Initial state preparation for quantum chemistry” `__. We will be expanding on that demo with code to import a state from the `complete active space configuration interaction `__ (CASCI) methods of PySCF, where we restrict the set of active orbitals used in the calculation. We start by creating our molecule object using the `Gaussian type orbitals `__ module ``pyscf.gto``, and obtaining the Hartree-Fock molecular orbitals with the `self-consistent field methods `__ ``pyscf.scf``. @@ -317,7 +317,7 @@ def initial_circuit(wf): # Next we will discuss how to prepare the electronic Hamiltonian for efficient time evolution in the Hadamard test circuit. # We will perform compressed double factorization (CDF) on the Hamiltonian to approximate it as a series of fragments, each of which can be fast-forwarded in a Trotter product formula. # -# If you haven’t yet, go read the demo `“How to build compressed double-factorized Hamiltonians” `__, because that is exactly what we are going to do! +# If you haven’t yet, go read the demo `“How to build compressed double-factorized Hamiltonians” `__, because that is exactly what we are going to do! # You could also look at Section III in Ref. [#Fomichev2025]_. # # Electronic Hamiltonian @@ -406,7 +406,7 @@ def initial_circuit(wf): # Figure 4: One- and two-electron fragment implementations in the time-evolution circuit (ignoring global phases). # Basis rotations are applied to both spin sections of the register. # -# We can use :class:`~pennylane.BasisRotation` to generate a `Givens decomposition `__ for the large unitary :math:`{\bf U}^{(\ell)}` that is generated by the single-particle basis rotation :math:`U^{(\ell)}` via Thouless' theorem. +# We can use :class:`~pennylane.BasisRotation` to generate a `Givens decomposition `__ for the large unitary :math:`{\bf U}^{(\ell)}` that is generated by the single-particle basis rotation :math:`U^{(\ell)}` via Thouless' theorem. # Note that we can do this separately for both spin-halves of the register, since our Hamiltonian does not mix spin sectors: this is cheaper than having one large unitary for the entire register. def U_rotations(U, control_wires): @@ -823,7 +823,7 @@ def final_state_overlap(ci_id): # # There are more optimizations for this algorithm that are included in Ref. [#Fomichev2025]_ that we did not implement in the above code. # One could further optimize the compressed double-factorized Hamiltonian by applying a block-invariant symmetry shift (BLISS) [#Loaiza2023]_ to the Hamiltonian prior to the factorization. -# This is already detailed in the `demo on CDF Hamiltonians `__. +# This is already detailed in the `demo on CDF Hamiltonians `__. # # Another optimization is to use a randomized second-order Trotter formula for the time evolution. # As discussed in Ref. [#Childs2019]_, the errors in deterministic product formulas scale with the number of commutators of the Hamiltonian terms. diff --git a/demonstrations_v2/tutorial_xas/metadata.json b/demonstrations_v2/tutorial_xas/metadata.json index 4294db5e48..38e3f4ceec 100644 --- a/demonstrations_v2/tutorial_xas/metadata.json +++ b/demonstrations_v2/tutorial_xas/metadata.json @@ -11,7 +11,7 @@ "executable_stable": true, "executable_latest": true, "dateOfPublication": "2025-08-29T09:00:00+00:00", - "dateOfLastModification": "2025-09-09T17:41:50+00:00", + "dateOfLastModification": "2025-09-22T15:48:14+00:00", "categories": [ "Quantum Chemistry", "Algorithms" diff --git a/demonstrations_v2/tutorial_zx_calculus/demo.py b/demonstrations_v2/tutorial_zx_calculus/demo.py index c2082a1fee..6f04aa2b7b 100644 --- a/demonstrations_v2/tutorial_zx_calculus/demo.py +++ b/demonstrations_v2/tutorial_zx_calculus/demo.py @@ -283,7 +283,7 @@ Now that we have all the necessary tools, let's see how to describe teleportation as a ZX-diagram and simplify it with our rewriting rules. The results are surprisingly elegant! We follow the explanation from [#JvdW2020]_. You can find an introduction to teleportation in -`the MBQC demo `__. +`the MBQC demo `__. Teleportation is a protocol for transferring quantum information (a state) from Alice (the sender) to Bob (the receiver). To perform this, Alice and Bob first need to share a maximally entangled state. The protocol for Alice to send diff --git a/demonstrations_v2/tutorial_zx_calculus/metadata.json b/demonstrations_v2/tutorial_zx_calculus/metadata.json index dee4fb74fc..c5bd1ba25f 100644 --- a/demonstrations_v2/tutorial_zx_calculus/metadata.json +++ b/demonstrations_v2/tutorial_zx_calculus/metadata.json @@ -8,7 +8,7 @@ "executable_stable": true, "executable_latest": true, "dateOfPublication": "2023-06-06T00:00:00+00:00", - "dateOfLastModification": "2025-09-09T17:41:50+00:00", + "dateOfLastModification": "2025-09-22T15:48:14+00:00", "categories": [ "Quantum Computing" ], diff --git a/demonstrations_v2/vqe_parallel/metadata.json b/demonstrations_v2/vqe_parallel/metadata.json index a166911950..99a308023e 100644 --- a/demonstrations_v2/vqe_parallel/metadata.json +++ b/demonstrations_v2/vqe_parallel/metadata.json @@ -8,7 +8,7 @@ "executable_stable": false, "executable_latest": false, "dateOfPublication": "2020-02-14T00:00:00+00:00", - "dateOfLastModification": "2025-09-09T17:41:50+00:00", + "dateOfLastModification": "2025-09-22T15:48:14+00:00", "categories": [ "Quantum Chemistry" ], diff --git a/demonstrations_v2/zne_catalyst/metadata.json b/demonstrations_v2/zne_catalyst/metadata.json index 3ed76e01b6..d88077da8a 100644 --- a/demonstrations_v2/zne_catalyst/metadata.json +++ b/demonstrations_v2/zne_catalyst/metadata.json @@ -11,7 +11,7 @@ "executable_stable": false, "executable_latest": true, "dateOfPublication": "2024-11-15T00:00:00+00:00", - "dateOfLastModification": "2024-11-25T09:00:00+00:00", + "dateOfLastModification": "2025-09-22T15:48:14+00:00", "categories": [ "Algorithms", "Quantum Computing" @@ -83,4 +83,4 @@ "weight": 1.0 } ] -} +} \ No newline at end of file