Update /qml/demos/ URLs to /demos/ across content (#1800)

Remove /qml prefix from all pennylane.ai demo URLs to match the current
site routing structure (pennylane.ai/demos/ instead of
pennylane.ai/qml/demos/).

* Authored by Cursor 🤖

---------

Co-authored-by: David <david.ren@xanadu.ai>
Co-authored-by: Paul Finlay <50180049+doctorperceptron@users.noreply.github.com>
Co-authored-by: Anton <anton.moiseev@xanadu.ai>
Co-authored-by: drdren <104710745+drdren@users.noreply.github.com>

Author: 5 people

conf.py

@@ -222,7 +222,7 @@
         + ("latest/" if dev else "stable/"),
         None,
     ),
-    "demo": ("https://pennylane.ai/qml", None),
+    "demo": ("https://pennylane.ai", "https://pennylane.ai/qml/objects.inv"),
 }
 
 # Enable :doc: references for intersphinx (disabled by default in Sphinx 5.0+)

demonstrations_metadata.md

@@ -61,7 +61,7 @@ Below is given an example of a complete metadata file for a demonstration.
     "hardware": [
         {
             "id": "aws",
-            "link": "https://pennylane.ai/qml/demos/tutorial_quantum_transfer_learning",
+            "link": "https://pennylane.ai/demos/tutorial_quantum_transfer_learning",
             "logo": "/_static/hardware_logos/aws.png"
         }
     ]

demonstrations_v2/adjoint_diff_benchmarking/demo.py

@@ -13,7 +13,7 @@
 
 ##############################################################################
 # This page is supplementary material to the
-# `Adjoint Differentiation <https://pennylane.ai/qml/demos/tutorial_adjoint_diff>`__
+# :doc:`Adjoint Differentiation <demos/tutorial_adjoint_diff>`
 # demonstration.  The below script produces the benchmarking images used.
 
 import timeit

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": "2026-04-14T15:48:14+00:00",
+    "dateOfLastModification": "2026-05-28T00:00:00+00:00",
     "categories": [
         "Quantum Machine Learning"
     ],

demonstrations_v2/circuits_as_fourier_series/demo.py

@@ -790,8 +790,8 @@
 
 Handily, both references have an associated PennyLane demo!
 
-- `Quantum models as Fourier series <https://pennylane.ai/qml/demos/tutorial_expressivity_fourier_series>`__ (2020). Maria Schuld, Ryan Sweke, Johannes Jakob Meyer.
-- `Generalized parameter-shift rules <https://pennylane.ai/qml/demos/tutorial_general_parshift>`__ (2021). David Wierichs.
+- :doc:`Quantum models as Fourier series <demos/tutorial_expressivity_fourier_series>` (2020). Maria Schuld, Ryan Sweke, Johannes Jakob Meyer.
+- :doc:`Generalized parameter-shift rules <demos/tutorial_general_parshift>` (2021). David Wierichs.
 
 Finally, PennyLane is jam-packed with tools for analyzing circuits as Fourier series.
 Check out the documentation on the :mod:`Fourier module <pennylane.fourier>` to learn more!

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": "2026-05-25T15:00:00+00:00",
+    "dateOfLastModification": "2026-05-28T00:00:00+00:00",
     "categories": [
         "Optimization",
         "Quantum Machine Learning"

demonstrations_v2/covalent_cloud_gpu/demo.py

@@ -11,8 +11,7 @@
 # QSVMs are essentially `traditional
 # SVMs <https://en.wikipedia.org/wiki/Support_vector_machine>`__ that rely on `embedding
 # kernels <https://en.wikipedia.org/wiki/Kernel_method#Mathematics:_the_kernel_trick>`__ evaluated on
-# a quantum computer—a.k.a. `quantum embedding
-# kernel <https://pennylane.ai/qml/demos/tutorial_kernels_module/#training-and-evaluating-quantum-kernels>`__.
+# a quantum computer—a.k.a. :doc:`quantum embedding kernel <demos/tutorial_kernels_module>`.
 # These kernels provide a unique (and perhaps classically intractable) means of measuring pairwise
 # similarity.
 #

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": "2026-04-14T15:48:14+00:00",
+    "dateOfLastModification": "2026-05-28T00:00:00+00:00",
     "categories": [
         "Quantum Machine Learning"
     ],

demonstrations_v2/gqe_training/demo.py

@@ -5,7 +5,7 @@
 `The generative quantum eigensolver (GQE) and its application for ground state search <https://arxiv.org/abs/2401.09253>`__
 that employs a classical generative model of quantum circuits for the purpose of ground-state
 energy estimation of any molecular Hamiltonian [#nakaji2024]_.  
-It has been proposed as a scalable alternative to the `variational quantum eigensolver (VQE) <https://pennylane.ai/qml/demos/tutorial_vqe/>`__ approach,
+It has been proposed as a scalable alternative to the :doc:`variational quantum eigensolver (VQE) <demos/tutorial_vqe>` approach,
 where the quantum state is represented as a quantum circuit with tunable parameters which are then optimized during training in order to arrive at a 
 state minimizing the corresponding energy :math:`E.` Instead, in GQE, the structure of the quantum circuit is given by a trained generative model.
 

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": "2026-04-14T15:48:14+00:00",
+    "dateOfLastModification": "2026-05-28T00:00:00+00:00",
     "categories": [
         "Quantum Machine Learning",
         "Quantum Chemistry",