Initialize

2026-04-15 21:10:21 +08:00
commit c4a82614b3
47 changed files with 9702 additions and 0 deletions
--- a/doc/source/.gitignore
+++ b/doc/source/.gitignore
@@ -0,0 +1 @@
+api-reference/
--- a/doc/source/QiboTN.png
+++ b/doc/source/QiboTN.png
--- a/doc/source/_static/css/style.css
+++ b/doc/source/_static/css/style.css
@@ -0,0 +1,7 @@
+.wy-side-nav-search {
+    background-color: #6400FF;
+}
+
+.wy-nav-top {
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+}
--- a/doc/source/_static/qibo_logo_dark.svg
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--- a/doc/source/conf.py
+++ b/doc/source/conf.py
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+# Configuration file for the Sphinx documentation builder.
+#
+# For the full list of built-in configuration values, see the documentation:
+# https://www.sphinx-doc.org/en/master/usage/configuration.html
+
+# -- Path setup --------------------------------------------------------------
+
+# If extensions (or modules to document with autodoc) are in another directory,
+# add these directories to sys.path here. If the directory is relative to the
+# documentation root, use os.path.abspath to make it absolute, like shown here.
+#
+from pathlib import Path
+
+from sphinx.ext import apidoc
+
+import qibotn
+
+# sys.path.insert(0, os.path.abspath(".."))
+
+
+# -- Project information -----------------------------------------------------
+# https://www.sphinx-doc.org/en/master/usage/configuration.html#project-information
+
+project = "Qibotn"
+copyright = "The Qibo team"
+author = "The Qibo team"
+
+# The full version, including alpha/beta/rc tags
+release = qibotn.__version__
+
+# -- General configuration ---------------------------------------------------
+# https://www.sphinx-doc.org/en/master/usage/configuration.html#general-configuration
+
+
+master_doc = "index"
+
+# Add any Sphinx extension module names here, as strings. They can be
+# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom
+# ones.
+extensions = [
+    "sphinx.ext.autodoc",
+    "sphinx.ext.doctest",
+    "sphinx.ext.coverage",
+    "sphinx.ext.napoleon",
+    "sphinx.ext.intersphinx",
+    "sphinx_copybutton",
+    "sphinxcontrib.katex",
+]
+
+templates_path = ["_templates"]
+exclude_patterns = []
+
+
+# -- Options for HTML output -------------------------------------------------
+# https://www.sphinx-doc.org/en/master/usage/configuration.html#options-for-html-output
+
+html_theme = "furo"
+html_favicon = "favicon.ico"
+
+# custom title
+html_title = "QiboTN · v" + release
+
+html_static_path = ["_static"]
+html_show_sourcelink = False
+
+html_theme_options = {
+    "top_of_page_button": "edit",
+    "source_repository": "https://github.com/qiboteam/qibotn/",
+    "source_branch": "main",
+    "source_directory": "doc/source/",
+    "light_logo": "qibo_logo_dark.svg",
+    "dark_logo": "qibo_logo_light.svg",
+    "light_css_variables": {
+        "color-brand-primary": "#6400FF",
+        "color-brand-secondary": "#6400FF",
+        "color-brand-content": "#6400FF",
+    },
+    "footer_icons": [
+        {
+            "name": "GitHub",
+            "url": "https://github.com/qiboteam/qibotn",
+            "html": """
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+}
+
+autodoc_mock_imports = ["cupy", "cuquantum"]
+
+
+def run_apidoc(_):
+    """Extract autodoc directives from package structure."""
+    source = Path(__file__).parent
+    docs_dest = source / "api-reference"
+    package = source.parents[1] / "src" / "qibotn"
+    apidoc.main(["--no-toc", "--module-first", "-o", str(docs_dest), str(package)])
+
+
+def setup(app):
+    app.add_css_file("css/style.css")
+
+    app.connect("builder-inited", run_apidoc)
--- a/doc/source/favicon.ico
+++ b/doc/source/favicon.ico
--- a/doc/source/getting-started/index.rst
+++ b/doc/source/getting-started/index.rst
@@ -0,0 +1,12 @@
+Getting started
+===============
+
+
+In this section we present the basic aspects of the Qibotn design and provide installation instructions.
+Please visit the following sections to understand how ``qibotn`` works.
+
+.. toctree::
+    :maxdepth: 1
+
+    installation
+    quickstart
--- a/doc/source/getting-started/installation.rst
+++ b/doc/source/getting-started/installation.rst
@@ -0,0 +1,10 @@
+Installation instructions
+=========================
+
+QiboTN can be installed directly from the source repository on Github:
+
+.. code-block::
+
+    git clone https://github.com/qiboteam/qibotn.git
+    cd qibotn
+    poetry install
--- a/doc/source/getting-started/quickstart.rst
+++ b/doc/source/getting-started/quickstart.rst
@@ -0,0 +1,138 @@
+Quick start
+===========
+
+In this section, we provide examples on how to use Qibotn to execute tensor network
+simulation of quantum circuit. First, we show how to use the Cutensornet and Quimb
+backends, while in a second moment we show a complete example of usage of the Quantum
+Matcha Tea Backend.
+
+Setting the backend with Cutensornet and Quimb
+""""""""""""""""""""""""""""""""""""""""""""""
+
+Among the backends provided by Qibotn, we have cutensornet (using cuQuantum library)
+and qutensornet (using Quimb library) for tensor network based simulations.
+At present, cutensornet backend works only for GPUs whereas qutensornet for CPUs.
+These backend can be set using the following command line.
+
+To use cuQuantum library, cutensornet can be specified as follows::
+
+   qibo.set_backend(
+      backend="qibotn", platform="cutensornet", runcard=computation_settings
+   )
+
+Similarly, to use Quimb library, qutensornet can be set as follows::
+
+   qibo.set_backend(
+       backend="qibotn", platform="qutensornet", runcard=computation_settings
+   )
+
+Setting the runcard
+"""""""""""""""""""
+
+The basic structure of the runcard is as follows::
+
+   computation_settings = {
+       "MPI_enabled": False,
+       "MPS_enabled": False,
+       "NCCL_enabled": False,
+       "expectation_enabled": {
+           "pauli_string_pattern": "IXZ",
+       },
+   }
+
+
+**MPI_enabled:** Setting this option *True* results in parallel execution of circuit using MPI (Message Passing Interface). At present, only works for cutensornet platform.
+
+**MPS_enabled:** This option is set *True* for Matrix Product State (MPS) based calculations where as general tensor network structure is used for *False* value.
+
+**NCCL_enabled:** This is set *True* for cutensoret interface for further acceleration while using Nvidia Collective Communication Library (NCCL).
+
+**expectation_enabled:** This option is set *True* while calculating expecation value of the circuit. Observable whose expectation value is to be calculated is passed as a string in the dict format as {"pauli_string_pattern": "observable"}. When the option is set *False*, the dense vector state of the circuit is calculated.
+
+
+Basic example
+"""""""""""""
+
+The following is a basic example to execute a two qubit circuit and print the final state in dense vector form using quimb backend::
+
+   import qibo
+   from qibo import Circuit, gates
+
+   # Set the runcard
+   computation_settings = {
+       "MPI_enabled": False,
+       "MPS_enabled": False,
+       "NCCL_enabled": False,
+       "expectation_enabled": False,
+   }
+
+
+   # Set the quimb backend
+   qibo.set_backend(
+       backend="qibotn", platform="qutensornet", runcard=computation_settings
+   )
+
+
+   # Construct the circuit with two qubits
+   c = Circuit(2)
+
+   # Apply Hadamard gates on first and second qubit
+   c.add(gates.H(0))
+   c.add(gates.H(1))
+
+   # Execute the circuit and obtain the final state
+   result = c()
+
+   # Print the final state
+   print(result.state())
+
+
+Using the Quantum Matcha Tea backend
+""""""""""""""""""""""""""""""""""""
+
+In the following we show an example of how the Quantum Matcha Tea backend can be
+used to execute a quantum circuit::
+
+    # We need Qibo to setup the circuit and the backend
+    from qibo import Circuit, gates
+    from qibo.models.encodings import ghz_state
+    from qibo.backends import construct_backend
+
+    # We need Quantum Matcha Tea to customize the tensor network simulation
+    from qmatchatea import QCConvergenceParameters
+
+    # Set the number of qubits
+    nqubits = 40
+
+    # Construct a circuit preparing a Quantum Fourier Transform
+    circuit = ghz_state(nqubits)
+
+    # Construct the backend
+    backend = construct_backend(backend="qibotn", platform="qmatchatea")
+
+    # Customize the low-level backend preferences according to Qibo's formalism
+    backend.set_device("/CPU:1")
+    backend.set_precision("double")
+
+    # Customize the tensor network simulation itself
+    backend.configure_tn_simulation(
+        ansatz = "MPS",
+        convergence_params = QCConvergenceParameters(max_bond_dimension=50, cut_ratio=1e-6)
+    )
+
+    # Execute the tensor network simulation
+    outcome = backend.execute_circuit(
+        circuit = circuit,
+        nshots=1024,
+    )
+
+    # Print some results
+    print(outcome.probabilities())
+    # Should print something like: {'0000000000000000000000000000000000000000': 0.5000000000000001, '1111111111111111111111111111111111111111': 0.5000000000000001}
+    print(outcome.frequencies())
+    # Should print something like: {'0000000000000000000000000000000000000000': 488, '1111111111111111111111111111111111111111': 536}
+
+
+By default, the simulator is choosing a specific method to compute the probabilities,
+and for further information we recommend the user to refer to our High-Level-API
+docstrings: :doc:`/api-reference/qibotn.backends`.
--- a/doc/source/index.rst
+++ b/doc/source/index.rst
@@ -0,0 +1,101 @@
+.. title::
+   QiboTN
+
+What is QiboTN?
+===============
+
+Qibotn is an high-level library which integrates tensor network simulation within
+the `Qibo <https://github.com/qiboteam/qibo>`_ ecosystem.
+
+If you are familiar with Qibo, you will be well aware of the modularity we provide
+through the use of our backends: after building a specific algorithm or quantum
+circuit, any of our backends can be selected to perform operations on the
+desired hardware (classical or quantum).
+
+Here, we extend this modularity to one of the most famous quantum inspired simulation
+technique.
+
+We do this by relying on well-known and maintained packages, and integrating their
+operation into our own dedicated backends.
+
+.. image:: QiboTN.png
+
+
+As shown in the figure above, we currently support three different backends, which
+correspond to the three mentioned packages:
+   - `cuQuantum <https://github.com/NVIDIA/cuQuantum>`_: an NVIDIA SDK of optimized libraries and tools for accelerating quantum computing workflows (we refer to the specific `Cutensornet <https://docs.nvidia.com/cuda/cuquantum/latest/cutensornet/index.html>`_  library);
+   - `quimb <https://quimb.readthedocs.io/en/latest/>`_: an easy but fast python library for ‘quantum information many-body’ calculations, focusing primarily on tensor networks;
+   - `Quantum Matcha Tea <https://www.quantumtea.it/>`_: a logical quantum computer emulator powered by matrix product states. Read `here <https://github.com/qiboteam/qibotn/blob/restore/examples/qmatchatea_intro/qmatchatea_introduction.ipynb>`_ if you want to have an example on how using the Quantum Matcha Tea backend.
+
+..  warning::
+
+   There are currently two ways to use the three backends (`qmatchatea` is
+   slightly different from the others), but we are working to standardize the interface.
+
+Thanks to the mentioned packages, we currently support some tensor network ansatze:
+Matrix Product States (MPS) on any mentioned backend, Tree Tensor Networks (TTN)
+through the Quantum Matcha Tea backend and a more general Tensor Network (TN) ansatz through
+Cutensornet and Quimb.
+
+Supported simulation features
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+We support Tensor Network contractions to:
+
+- dense vectors (all the backends)
+- expecation values of given Pauli string (Cutensornet and Qmatchatea)
+
+The supported HPC configurations are:
+
+- single-node CPU through Quimb and Qmatchatea
+- single-node GPU or GPUs through Cutensornet and Qmatchatea
+- multi-node multi-GPU with Message Passing Interface (MPI) through Cutensornet
+- multi-node multi-GPU with NVIDIA Collective Communications Library (NCCL) through Cutensornet
+
+
+How to Use the Documentation
+============================
+
+Welcome to the comprehensive documentation for QiboTN! This guide will help you navigate through the various sections and make the most of the resources available.
+
+
+1. **Getting started**: Begin by referring to the
+   :doc:`/getting-started/installation/` guide to set up the ``QiboTN`` library in your environment.
+
+2. **Tutorials**: Explore the :doc:`getting-started/quickstart/` section for basic usage examples
+
+
+Contents
+--------
+
+.. toctree::
+   :maxdepth: 2
+   :caption: Introduction
+
+   getting-started/index
+
+.. toctree::
+    :maxdepth: 1
+    :caption: Main documentation
+
+    api-reference/qibotn
+    Developer guides <https://qibo.science/qibo/stable/developer-guides/index.html>
+
+.. toctree::
+    :maxdepth: 1
+    :caption: Documentation links
+
+    Qibo docs <https://qibo.science/qibo/stable/>
+    Qibolab docs <https://qibo.science/qibolab/stable/>
+    Qibocal docs <https://qibo.science/qibocal/stable/>
+    Qibosoq docs <https://qibo.science/qibosoq/stable/>
+    Qibochem docs <https://qibo.science/qibochem/stable/>
+    Qibotn docs <https://qibo.science/qibotn/stable/>
+    Qibo-cloud-backends docs <https://qibo.science/qibo-cloud-backends/stable/>
+
+Indices and tables
+==================
+
+* :ref:`genindex`
+* :ref:`modindex`
+* :ref:`search`