PennyLaneAI · Alan-eMartin · Sep 23, 2025 · Sep 18, 2025 · Sep 18, 2025 · Sep 18, 2025
diff --git 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
@@ -57,7 +57,7 @@
 more information on pulse programming in PennyLane, see the
 `PennyLane docs <https://docs.pennylane.ai/en/stable/code/qml_pulse.html>`__, or check out the demo
 about
-`running a ctrl-VQE algorithm with pulse control <https://pennylane.ai/qml/demos/tutorial_pulse_programming101.html>`__ on the PennyLane `default.qubit` simulator.
+:doc:`running a ctrl-VQE algorithm with pulse control <demos/tutorial_pulse_programming101>` 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 <https://pennylane.ai/qml/demos/tutorial_pasqal.html>`_ to determine
+:doc:`arbitrary user-specified geometries <demos/tutorial_pasqal>` 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
@@ -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
@@ -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
@@ -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
@@ -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
@@ -46,7 +46,7 @@
 
 ##############################################################################
 # This tutorial requires the ``pennylane-rigetti`` and ``pennylane-qiskit`` packages, which can be
-# installed by following the instructions `here <https://pennylane.ai/install.html>`__. We also
+# installed by following the instructions `here <https://pennylane.ai/install>`__. We also
 # make use of the `PyTorch interface <https://pennylane.readthedocs.io/en/stable/introduction
 # /interfaces.html>`_, which can be installed from `here
 # <https://pytorch.org/get-started/locally/>`__.

diff --git 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
@@ -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 <https://pennylane.ai/qml/demos/quantum_neural_net.html>`__
+# :doc:`demo <demos/quantum_neural_net>`
 # 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
@@ -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
@@ -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
@@ -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
@@ -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
@@ -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
@@ -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
@@ -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
@@ -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 <demos/tutorial_qaoa_intro>`.
 
 .. 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/ <PennyLane Demo — QAOA for MaxCut>`__.
+#       MaxCut problem: :doc:`demos/tutorial_qaoa_maxcut`.
 #
 #
 #

diff --git 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
@@ -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
@@ -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
@@ -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
@@ -19,8 +19,8 @@
 plugins. We first introduce PennyLane's `Strawberry Fields plugin <https://pennylane-sf.readthedocs.io>`_
 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 <qubit_rotation>` and
-:ref:`Gaussian transformation <gaussian_transformation>` tutorials before attempting this tutorial.
+Be sure to read through the introductory :doc:`qubit rotation <demos/tutorial_qubit_rotation>` and
+:doc:`Gaussian transformation <demos/tutorial_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 <qubit_rotation>` circuit:
+:doc:`qubit rotation <demos/tutorial_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 <qubit_rotation>` with the CV photon-redirection
+# from the :doc:`qubit rotation tutorial <demos/tutorial_qubit_rotation>` 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
@@ -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
@@ -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 <https://amazon-braket-pennylane-plugin-python.readthedocs.io/en/stable/index.html>`__ instead.
 
 
-Let's revisit the original :ref:`qubit rotation <qubit_rotation>` tutorial, but instead of
+Let's revisit the original :doc:`qubit rotation <demos/tutorial_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 <https://pennylane.ai/qml/demos/braket-parallel-gradients.html>`_.
+# :doc:`Computing gradients in parallel with Amazon Braket <demos/braket-parallel-gradients>`.
 
 import pennylane as qml
 import torch

diff --git 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
@@ -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
@@ -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
@@ -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
@@ -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
@@ -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
@@ -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
@@ -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
@@ -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
@@ -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/...trations_v2/tutorial_How_to_optimize_QML_model_using_JAX_catalyst_and_Optax/metadata.json b/...trations_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/...strations_v2/tutorial_How_to_simulate_quantum_circuits_with_tensor_networks/metadata.json b/...strations_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
@@ -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
@@ -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"
     ],