Cherry-pick cosmetic changes to docs

docs/_static/nav-config.js

@@ -3,11 +3,11 @@ const navConfig = {
 
         {
             "title": "Getting started",
-            "href": "installation.html",
+            "href": "intro.html",
         },
         {
-            "title": "NLP 101",
-            "href": "nlp-intro.html",
+            "title": "User guide",
+            "href": "pipeline.html",
         },
         {
             "title": "Tutorials",

docs/conf.py

@@ -87,6 +87,10 @@
   'conf_py_path': '/docs/'
 }
 
+html_additional_pages = {
+  "index": "lambeq_landing_page.html"
+}
+
 # Add any paths that contain custom static files (such as style sheets) here,
 # relative to this directory. They are copied after the builtin static files,
 # so a file named "default.css" will overwrite the builtin "default.css".

docs/index.rst

@@ -1,59 +1,20 @@
-lambeq
-======
-
-.. image:: _static/images/Quantinuum_logo.png
-   :width: 240px
-   :align: right
-
-``lambeq`` is an open-source, modular, extensible high-level Python library for experimental :term:`Quantum Natural Language Processing <quantum NLP (QNLP)>` (QNLP), created by `Quantinuum <https://www.quantinuum.com>`_'s QNLP team. At a high level, the library allows the conversion of any sentence to a :term:`quantum circuit`, based on a given :term:`compositional model` and certain parameterisation and choices of :term:`ansätze <ansatz (plural: ansätze)>`, and facilitates :ref:`training <sec-training>` for both quantum and classical NLP experiments. The notes for the latest release can be found :ref:`here <sec-release-notes>`.
-
-``lambeq`` is available for Python 3.10 and higher, on Linux, macOS and Windows. To install, type:
-
-.. code-block:: bash
-
-   pip install lambeq
-
-or refer to :ref:`sec-installation` for more information. To start the tutorial, go to `Step 1: Sentence Input <tutorials/sentence-input.ipynb>`_. To see the example notebooks, go to :ref:`sec-examples`. To use the command-line interface, read :ref:`sec-cli`. To make your own contributions to ``lambeq``, see :ref:`sec-contributing`.
-
-.. note::
-   Please do not try to read this documentation directly from the preview provided in the `GitHub repository <https://github.com/CQCL/lambeq/tree/main/docs>`_, since some of the pages will not be rendered properly.
-
-User support
-------------
-
-If you need help with ``lambeq`` or you think you have found a bug, please send an email to [email protected]. You can also open an issue at ``lambeq``'s `GitHub repository <https://github.com/CQCL/lambeq>`_. Someone from the development team will respond to you as soon as possible. Furthermore, if you want to subscribe to ``lambeq``'s mailing list ([email protected]), send an email to [email protected] to let us know.
-
-Note that the best way to get in touch with the QNLP community and learn about ``lambeq`` is to join our `QNLP discord server <https://discord.gg/TA63zghMrC>`_, where you can ask questions, get notified about important announcements and news, and chat with other QNLP researchers.
-
-Licence
--------
-
-Licensed under the `Apache 2.0 License <http://www.apache.org/licenses/LICENSE-2.0>`_.
-
-How to cite
------------
-If you use ``lambeq`` for your research, please cite the accompanying paper [Kea2021]_:
-
-.. code-block:: bash
-
-   @article{kartsaklis2021lambeq,
-      title={lambeq: {A}n {E}fficient {H}igh-{L}evel {P}ython {L}ibrary for {Q}uantum {NLP}},
-      author={Dimitri Kartsaklis and Ian Fan and Richie Yeung and Anna Pearson and Robin Lorenz and Alexis Toumi and Giovanni de Felice and Konstantinos Meichanetzidis and Stephen Clark and Bob Coecke},
-      year={2021},
-      journal={arXiv preprint arXiv:2110.04236},
-   }
-
 .. toctree::
    :caption: Getting started
    :maxdepth: 1
 
+   intro
    installation
    troubleshooting
+   CONTRIBUTING
+
+.. toctree::
+   :caption: User guide
+   :maxdepth: 1
+
    pipeline
-   parsing
    string-diagrams
+   parsing
    use-cases
-   CONTRIBUTING
 
 .. toctree::
    :caption: NLP-101

docs/use-cases.rst

@@ -156,11 +156,10 @@ As soon as you are satisfied with the results of the simulations, it's time for
 .. csv-table:: Quantum platforms.
    :header: "Platform", "Technology"
    :widths: 30, 60
-   :align: center
 
    "`Alpine Quantum Technologies <https://www.aqt.eu/qc-systems/>`_", "`Trapped ions <https://en.wikipedia.org/wiki/Trapped_ion_quantum_computer>`_"
    "`Amazon Braket <https://aws.amazon.com/braket/>`_", "`Annealing <https://en.wikipedia.org/wiki/Quantum_annealing>`_, trapped ions, `superconducting qubits <https://en.wikipedia.org/wiki/Superconducting_quantum_computing>`_, `photonics <https://pennylane.ai/qml/demos/tutorial_photonics.html>`_"
-   "`Atom Computing <https://atom-computing.com/>`_", "`Neutral atoms <https://pennylane.ai/qml/demos/tutorial_pasqal.html>`_" in an "`optical lattice <https://en.wikipedia.org/wiki/Optical_lattice>`_"
+   "`Atom Computing <https://atom-computing.com/>`_", "`Neutral atoms <https://pennylane.ai/qml/demos/tutorial_pasqal.html>`_ in an `optical lattice <https://en.wikipedia.org/wiki/Optical_lattice>`_"
    "`Google Quantum AI <https://quantumai.google/quantum-computing-service>`_", "Superconducting qubits"
    "`IBM Quantum <https://quantum-computing.ibm.com>`_", "Superconducting qubits"
    "`IonQ Cloud access <https://ionq.com/get-started/#cloud-access>`_", "Trapped ions"
@@ -170,7 +169,7 @@ As soon as you are satisfied with the results of the simulations, it's time for
    "`Quandela <https://www.quandela.co/>`_", "Photonics"
    "`Quantinuum <https://www.honeywell.com/us/en/company/quantum>`_", "Trapped ions"
    "`Quantware <https://www.quantware.eu>`_", "Superconducting qubits"
-   "`QuEra <https://www.quera.com>`_", Neutral atoms
+   "`QuEra <https://www.quera.com>`_", "Neutral atoms"
    "`Rigetti Quantum Cloud Services <https://qcs.rigetti.com/sign-in>`_", "Superconducting qubits"
 
 .. rubric:: See also: