Improve quickstart - Introduction (#2530)

* change introduction

* add pics

* figure out best figure placement

* improve first sentence

* Update doc/introduction/pennylane.rst

* Update doc/introduction/pennylane.rst

* Update doc/introduction/pennylane.rst

* finalise first sentence

Co-authored-by: Josh Izaac <josh146@gmail.com>

doc/introduction/pennylane.rst

@@ -3,85 +3,44 @@
 
 .. _pl_intro:
 
-Introduction
-============
+What is PennyLane?
+==================
 
-PennyLane provides a bridge between classical and quantum computations, making it
-easy to build and optimize hybrid computations. Prominent examples are
-*variational quantum eigensolvers* and *quantum machine learning models*.
-
-Bridging the classical and quantum world
-----------------------------------------
-
-Classical computations, as well as optimization or training of models, are executed using
-one of the standard scientific computing or machine learning libraries. PennyLane provides an
-**interface** to these libraries, making them quantum-aware.
-
-PennyLane's standard interface is `NumPy <https://numpy.org/>`_,
-but interfaces to powerful machine learning libraries like `PyTorch <https://pytorch.org/>`_,
-`Tensorflow <https://www.tensorflow.org/>`_, and `JAX <https://github.com/google/jax>`__ are also available.
-
-The quantum computations are sent to a **device** for execution. A device can be real quantum
-hardware or a classical simulator. PennyLane comes with default simulator devices,
-but is well-integrated with external software and hardware to run quantum
-circuits---such as Xanadu's Strawberry Fields, Rigetti's Forest, IBM's Qiskit, or Google's Cirq.
-
-Communication between the classical computing library and quantum devices is coordinated in
-PennyLane via a suite of **plugins**:
-
-.. image:: ../_static/jigsaw.png
-    :align: center
-    :width: 90%
+.. figure:: ../_static/code.png
+    :align: left
+    :figwidth: 350px
+    :width: 300px
     :target: javascript:void(0);
 
 |
 
-The main job of PennyLane is to manage the evaluation of parametrized quantum circuits
-(so-called *variational circuits*) on quantum devices,
-and to make them accessible to the machine learning libraries.
-PennyLane also provides access to gradients of quantum circuits, which the machine
-learning library can use to perform backpropagation, including through quantum
-circuits---an essential process for optimization and machine learning.
+PennyLane is a cross-platform Python library for programming quantum computers.
+Its differentiable programming paradigm enables the execution and training of quantum programs on various backends.
+
+PennyLane connects quantum computing with powerful machine learning frameworks
+like `NumPy <https://numpy.org/>`_'s `autograd <https://github.com/HIPS/autograd>`__,
+`JAX <https://github.com/google/jax>`__,
+`PyTorch <https://pytorch.org/>`_, and `TensorFlow <https://www.tensorflow.org/>`_,
+making them quantum-aware.
 
-More details
-------------
+Its central job is to manage the execution of quantum computations, including
+the evaluation of circuits and the computation of their gradients.
+This information is forwarded to the classical
+framework, creating seamless quantum-classical pipelines for applications.
 
-In the following sections you can learn more about the key features of PennyLane:
+|
 
-.. image:: ../_static/code.png
+.. figure:: ../_static/jigsaw.png
     :align: right
-    :width: 300px
+    :figwidth: 500px
+    :width: 450px
     :target: javascript:void(0);
 
-1. :doc:`circuits` shows how PennyLane unifies and simplifies
-   the process of programming quantum circuits with trainable parameters.
-
-..
-
-2. :doc:`interfaces` introduces how PennyLane is used with different
-   optimization libraries to optimize quantum circuits or hybrid computations.
-
-..
-
-3. :doc:`operations` outlines the various quantum circuit building blocks
-   provided in PennyLane.
-
-..
-
-4. :doc:`measurements` presents the different options available to measure
-   the output of quantum circuits.
-
-..
-
-5. :doc:`templates` gives an overview of different larger-scale composable
-   layers for building quantum algorithms.
-
-..
-
-6. :doc:`optimizers` details the built-in tools for optimizing and training
-   quantum computing and quantum machine learning circuits.
-
-..
+PennyLane's design principle states that
+circuits can be run on various kinds of simulators or hardware devices without making any changes --
+the complex job of optimising communication with the devices, compiling circuits to suit the backend,
+and choosing the best gradient strategies is taken care of.
 
-7. :doc:`configuration` provides details about how to customize
-   PennyLane and provide credentials for quantum hardware access.
+The library comes with default simulator devices, but is well-integrated with
+`external software and hardware <https://pennylane.ai/plugins.html>`__ to run quantum
+circuits---such as IBM's Qiskit, or Google's Cirq, Rigetti's Forest, or Xanadu's Strawberry Fields.