PennyLaneAI · KetpuntoG · Jan 15, 2024 · Jan 16, 2024 · Jan 17, 2024 · Jan 17, 2024
diff --git a/demonstrations/adjoint_diff_benchmarking.py b/demonstrations/adjoint_diff_benchmarking.py
@@ -1,17 +1,7 @@
 r"""
-
-.. _adjoint_differentiation_benchmarking:
-
 Adjoint Differentiation
 =======================
 
-.. meta::
-    :property="og:description": Benchmarking file for adjoint diff demonstration.
-    :property="og:image": https://pennylane.ai/qml/_static/thumbs/code.png
-
-
-*Author: Christina Lee — Posted: 23 November 2021. Last updated: 23 November 2021.*
-
 """
 
 ##############################################################################
@@ -143,4 +133,4 @@ def circuit(params):
 #
 # About the author
 # ----------------
-# .. include:: ../_static/authors/christina_lee.txt
+#
diff --git a/demonstrations/ahs_aquila.py b/demonstrations/ahs_aquila.py
@@ -1,16 +1,6 @@
 r"""Pulse programming on Rydberg atom hardware
 ==============================================
 
-.. meta::
-    :property="og:description": Perform measurements on neutral atom hardware through PennyLane
-    :property="og:image": https://pennylane.ai/qml/_static/demonstration_assets//thumbnail_tutorial_pulse_on_hardware.png
-
-.. related::
-   tutorial_pasqal Quantum computation with neutral atoms
-   tutorial_pulse_programming101 Differentiable pulse programming with qubits in PennyLane
-
-*Author: Lillian M.A. Frederiksen — Posted: 16 May 2023.*
-
 Neutral atom hardware is a new innovation in quantum technology that has been gaining traction in
 recent years thanks to new developments in optical tweezer technology. One such device,
 `QuEra's Aquila <https://www.quera.com/aquila>`__, is capable of running circuits with up to 256
@@ -821,4 +811,4 @@ def circuit(params):
 ##############################################################################
 # About the author
 # ----------------
-# .. include:: ../_static/authors/lillian_frederiksen.txt
+#
diff --git a/demonstrations/braket-parallel-gradients.py b/demonstrations/braket-parallel-gradients.py
@@ -2,18 +2,6 @@
 Computing gradients in parallel with Amazon Braket
 ==================================================
 
-.. meta::
-    :property="og:description": Parallelize gradient calculations with Amazon Braket
-    :property="og:image": https://pennylane.ai/qml/_static/demonstration_assets//pl-braket.png
-
-.. related::
-
-    tutorial_qaoa_intro Intro to QAOA
-    vqe_parallel VQE with parallel QPUs with Rigetti
-
-
-*Authors: Tom Bromley and Maria Schuld — Posted: 08 December 2020. Last updated: 30 September 2021.*
-
 PennyLane integrates with `Amazon Braket <https://aws.amazon.com/braket/>`__ to enable quantum
 machine learning and optimization on high-performance simulators and quantum processing
 units (QPUs) through a range of `providers <https://aws.amazon.com/braket/hardware-providers/>`__.
@@ -608,8 +596,4 @@ def cost_function(params, **kwargs):
 ##############################################################################
 # About the authors
 # -----------------
-# .. include:: ../_static/authors/thomas_bromley.txt
-#
-# .. include:: ../_static/authors/maria_schuld.txt
-#
-# .. include:: ../_static/authors/matthew_beach.txt
+#
diff --git a/demonstrations/circuits_as_fourier_series.py b/demonstrations/circuits_as_fourier_series.py
@@ -6,10 +6,6 @@
 
     <script src="https://cdnjs.cloudflare.com/ajax/libs/p5.js/1.1.9/p5.js"></script>
 
-.. meta::
-    :property="og:description": Learn interactively how we can view circuits as Fourier series.
-    :property="og:image": https://pennylane.ai/qml/_static/demonstration_assets//thumbnail_tutorial_Fourier.png
-
 In this demo, we're going to give an interactive, code-free introduction to the idea
 of quantum circuits as Fourier series. We'll also discuss one of the main applications, called the
 parameter-shift rule. Concepts will be embodied in visualizations you can play with!
@@ -801,4 +797,3 @@
 # About the author
 # ----------------
 #
-# .. include:: ../_static/authors/david_wakeham.txt
diff --git a/demonstrations/ensemble_multi_qpu.py b/demonstrations/ensemble_multi_qpu.py
@@ -2,18 +2,6 @@
 Ensemble classification with Rigetti and Qiskit devices
 =======================================================
 
-.. meta::
-    :property="og:description": We demonstrate how two QPUs can be
-        combined in parallel to help solve a machine learning classification problem,
-        using PyTorch and PennyLane.
-    :property="og:image": https://pennylane.ai/qml/_static/demonstration_assets//ensemble_diagram.png
-
-.. related
-
-   tutorial_variational_classifier Variational classifier
-
-*Author: Tom Bromley — Posted: 14 February 2020. Last updated: 13 December 2021.*
-
 This tutorial outlines how two QPUs can be combined in parallel to help solve a machine learning
 classification problem.
 
@@ -572,4 +560,4 @@ def plot_points_prediction(x, y, p, title):
 ##############################################################################
 # About the author
 # ----------------
-# .. include:: ../_static/authors/thomas_bromley.txt
+#
diff --git a/demonstrations/function_fitting_qsp.py b/demonstrations/function_fitting_qsp.py
@@ -5,13 +5,6 @@
 Function Fitting using Quantum Signal Processing
 ======================================================
 
-.. meta::
-    :property="og:description": Learn how to create polynomial approximations to functions
-        using Quantum Signal Processing (QSP).
-    :property="og:image": https://pennylane.ai/qml/_static/demonstration_assets/function_fitting_qsp/cover.png
-
-*Author: Jay Soni — Posted: 24 May 2022. Last updated: 17 April 2023.*
-
 Introduction
 ~~~~~~~~~~~~~
 
@@ -576,5 +569,4 @@ def step_func(x):
 #
 # About the author
 # ~~~~~~~~~~~~~~~~
-# .. include:: ../_static/authors/jay_soni.txt
 #
diff --git a/demonstrations/gbs.py b/demonstrations/gbs.py
@@ -1,24 +1,7 @@
 r"""
-.. role:: html(raw)
-   :format: html
-
 Quantum advantage with Gaussian Boson Sampling
 ==============================================
 
-.. meta::
-    :property="og:description": Using light to perform tasks beyond the reach of classical computers.
-
-    :property="og:image": https://pennylane.ai/qml/_static/demonstration_assets//gbs_expt2.png
-
-.. related::
-
-    tutorial_gaussian_transformation Gaussian transformation
-    qsim_beyond_classical Beyond classical computing with qsim
-    qonn Optimizing a quantum optical neural network
-    tutorial_photonics Photonic quantum computers
-
-*Authors: Josh Izaac and Nathan Killoran — Posted: 04 December 2020. Last updated: 04 December 2020.*
-
 .. warning::
     This demo is only compatible with PennyLane version ``0.29`` or below.
 

diff --git a/demonstrations/getting_started_with_hybrid_jobs.py b/demonstrations/getting_started_with_hybrid_jobs.py
@@ -385,4 +385,4 @@ def circuit(params):
 ##############################################################################
 # About the author
 # ----------------
-# .. include:: ../_static/authors/matthew_beach.txt
+#
diff --git a/demonstrations/ibm_pennylane.py b/demonstrations/ibm_pennylane.py
@@ -2,17 +2,6 @@
 Using PennyLane with IBM's quantum devices and Qiskit
 ===================================
 
-.. meta::
-    :property="og:description": Learn how to use IBM devices with Pennylane.
-    :property="og:image": https://pennylane.ai/qml/_static/demonstration_assets//thumbnail_tutorial_ibm_pennylane.png
-
-.. related::
-
-   quantum_volume Quantum Volume
-   tutorial_vqe A brief overview of VQE
-
-*Authors: Kaur Kristjuhan, Clara Ferreira Cores, Mark Nicholas Jones; Molecular Quantum Solutions (MQS) — Posted: 20 June 2023. Last updated: 20 June 2023.*
-
 .. warning::
     This demo currently does not work as the Qiskit Runtime VQE program has been retired.
 
@@ -362,6 +351,4 @@ def cost_fn_2(theta):
 #
 # About the authors
 # ----------------
-# .. include:: ../_static/authors/kaur_kristjuhan.txt
-# .. include:: ../_static/authors/clara_ferreira_cores.txt
-# .. include:: ../_static/authors/mark_nicholas_jones.txt
+#
diff --git a/demonstrations/learning2learn.py b/demonstrations/learning2learn.py
@@ -2,18 +2,6 @@
 Learning to learn with quantum neural networks
 ==============================================
 
-.. meta::
-    :property="og:description": Use a classical recurrent neural network to initilize the parameters of a variational quatum algorithm.
-    :property="og:image": ../_static/demonstration_assets/learning2learn/thumbnail.png
-
-.. related::
-
-   tutorial_qaoa_intro Intro to QAOA
-   tutorial_qaoa_maxcut QAOA for MaxCut problem
-
-*Author: Stefano Mangini — Posted: 02 March 2021. Last updated: 15 September 2021.*
-
-
 In this demo we recreate the architecture proposed
 in *Learning to learn with quantum neural networks via
 classical neural networks* [#l2l]_, using **PennyLane** and **TensorFlow**.
@@ -1174,4 +1162,4 @@ def call(self, inputs):
 #
 # About the author
 # ----------------
-# .. include:: ../_static/authors/stefano_mangini.txt
+#
diff --git a/demonstrations/oqc_pulse.py b/demonstrations/oqc_pulse.py
@@ -1,16 +1,6 @@
 r"""Pulse programming on OQC Lucy in PennyLane
 ==============================================
 
-.. meta::
-    :property="og:description": Perform hardware-level pulse gates on superconducting qubits in PennyLane
-    :property="og:image": https://pennylane.ai/qml/_static/demonstration_assets//thumbnail_intro_oqc_pulse.png
-
-.. related::
-   ahs_aquila Pulse programming on Rydberg atom hardware
-   tutorial_pulse_programming101 Differentiable pulse programming with qubits in PennyLane
-
-*Author: Korbinian Kottmann — Posted: 30 October 2023.*
-
 Pulse-level access to quantum computers offers many interesting new avenues in
 quantum optimal control, variational quantum algorithms and device-aware algorithm design.
 We now have the possibility to run hardware-level circuits combined with standard gates on a
@@ -473,4 +463,4 @@ def qnode_lucy(params, duration=15.):
 #
 # About the author
 # ----------------
-# .. include:: ../_static/authors/korbinian_kottmann.txt
+#
diff --git a/demonstrations/plugins_hybrid.py b/demonstrations/plugins_hybrid.py
@@ -1,22 +1,7 @@
 r"""
-.. _plugins_hybrid:
-
 Plugins and hybrid computation
 ==============================
 
-.. meta::
-    :property="og:description": This tutorial introduces the notion of hybrid
-        computation by combining several PennyLane device backends to train an algorithm
-        containing both photonic and qubit devices.
-    :property="og:image": https://pennylane.ai/qml/_static/demonstration_assets//photon_redirection.png
-
-.. related::
-
-   tutorial_qubit_rotation Basic tutorial: qubit rotation
-   tutorial_gaussian_transformation Gaussian transformation
-
-*Author: Josh Izaac — Posted: 11 October 2019. Last updated: 01 February 2021.*
-
 This tutorial introduces the notion of hybrid computation by combining several PennyLane
 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
@@ -449,4 +434,4 @@ def cost(params, phi1=0.5, phi2=0.1):
 #
 # About the author
 # ----------------
-# .. include:: ../_static/authors/josh_izaac.txt
+#
diff --git a/demonstrations/pytorch_noise.py b/demonstrations/pytorch_noise.py
@@ -1,19 +1,7 @@
 """
-.. _pytorch_noise:
-
 PyTorch and noisy devices
 =========================
 
-.. meta::
-    :property="og:description": Extend PyTorch with real quantum computing power,
-        by using it to optimize a noisy quantum hardware device.
-    :property="og:image": https://pennylane.ai/qml/_static/demonstration_assets//bloch.gif
-
-.. related::
-
-   tutorial_noisy_circuit_optimization Optimizing noisy circuits with Cirq
-
-*Author: Josh Izaac — Posted: 11 October 2019. Last updated: 9 November 2022.*
 
 Let's revisit the original :ref:`qubit rotation <qubit_rotation>` tutorial, but instead of
 using the default NumPy/autograd QNode interface, we'll use the :doc:`introduction/interfaces/torch`.
@@ -244,4 +232,4 @@ def cost(phi, theta, step):
 #
 # About the author
 # ----------------
-# .. include:: ../_static/authors/josh_izaac.txt
+#
diff --git a/demonstrations/qnspsa.py b/demonstrations/qnspsa.py
@@ -2,16 +2,6 @@
 Quantum natural SPSA optimizer
 ==============================
 
-.. meta::
-    :property="og:description": Introduction to the Quantum natural SPSA optimizer, which reduces the number of quantum measurements in the optimization.
-    :property="og:image": https://pennylane.ai/qml/_static/demonstration_assets//qnspsa_cover.png
-
-.. related::
-
-   tutorial_spsa Simultaneous perturbation stochastic approximation (SPSA) optimizer
-
-*Author: Yiheng Duan — Posted: 18 July 2022. Last updated: 05 September 2022.*
-
 In this tutorial, we show how we can implement the
 `quantum natural simultaneous perturbation stochastic approximation (QN-SPSA) optimizer
 <https://quantum-journal.org/papers/q-2021-10-20-567/>`__
@@ -1043,4 +1033,4 @@ def __apply_blocking(self, cost, params_curr, params_next):
 #
 # About the author
 # ----------------
-# .. include:: ../_static/authors/yiheng_duan.txt
+#
diff --git a/demonstrations/qonn.py b/demonstrations/qonn.py
@@ -1,19 +1,7 @@
 """
-.. _quantum_optical_neural_network:
-
 Optimizing a quantum optical neural network
 ===========================================
 
-.. meta::
-    :property="og:description": Optimizing a quantum optical neural network using PennyLane.
-    :property="og:image": https://pennylane.ai/qml/_static/demonstration_assets//qonn_thumbnail.png
-
-.. related::
-
-   quantum_neural_net Function fitting with a photonic quantum neural network
-
-*Author: Theodor Isacsson — Posted: 05 August 2020. Last updated: 08 March 2022.*
-
 .. warning::
     This demo is only compatible with PennyLane version ``0.29`` or below.
 
@@ -474,4 +462,4 @@ def cost_wrapper(var, grad=[]):
 #
 # About the author
 # ----------------
-# .. include:: ../_static/authors/theodor_isacsson.txt
+#
diff --git a/demonstrations/qsim_beyond_classical.py b/demonstrations/qsim_beyond_classical.py
@@ -1,22 +1,7 @@
 """
-
-.. _qsim_beyond_classical:
-
 Beyond classical computing with qsim
 ====================================
 
-.. meta::
-    :property="og:description": Use Google's qsim simulator to explore the barriers between quantum and classical computing, and recreate their benchmarks and circuits.
-    :property="og:image": https://pennylane.ai/qml/_static/demonstration_assets//sycamore.png
-
-.. related::
-
-    tutorial_quantum_metrology Variationally optimizing measurement protocols
-    tutorial_noisy_circuit_optimization Optimizing noisy circuits with Cirq
-    quantum_volume Quantum volume
-
-*Author: Theodor Isacsson — Posted: 30 November 2020. Last updated: 10 September 2021.*
-
 .. figure:: ../_static/demonstration_assets/qsim_beyond_classical/qc.png
     :align: right
     :height: 300pt
@@ -583,4 +568,4 @@ def fidelity_xeb(samples, probs):
 #
 # About the author
 # ----------------
-# .. include:: ../_static/authors/theodor_isacsson.txt
+#