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102 lines
3.9 KiB
ReStructuredText
102 lines
3.9 KiB
ReStructuredText
.. title::
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QiboTN
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What is QiboTN?
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===============
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Qibotn is an high-level library which integrates tensor network simulation within
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the `Qibo <https://github.com/qiboteam/qibo>`_ ecosystem.
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If you are familiar with Qibo, you will be well aware of the modularity we provide
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through the use of our backends: after building a specific algorithm or quantum
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circuit, any of our backends can be selected to perform operations on the
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desired hardware (classical or quantum).
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Here, we extend this modularity to one of the most famous quantum inspired simulation
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technique.
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We do this by relying on well-known and maintained packages, and integrating their
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operation into our own dedicated backends.
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.. image:: QiboTN.png
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As shown in the figure above, we currently support three different backends, which
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correspond to the three mentioned packages:
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- `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);
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- `quimb <https://quimb.readthedocs.io/en/latest/>`_: an easy but fast python library for ‘quantum information many-body’ calculations, focusing primarily on tensor networks;
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- `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.
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.. warning::
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There are currently two ways to use the three backends (`qmatchatea` is
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slightly different from the others), but we are working to standardize the interface.
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Thanks to the mentioned packages, we currently support some tensor network ansatze:
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Matrix Product States (MPS) on any mentioned backend, Tree Tensor Networks (TTN)
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through the Quantum Matcha Tea backend and a more general Tensor Network (TN) ansatz through
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Cutensornet and Quimb.
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Supported simulation features
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^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
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We support Tensor Network contractions to:
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- dense vectors (all the backends)
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- expecation values of given Pauli string (Cutensornet and Qmatchatea)
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The supported HPC configurations are:
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- single-node CPU through Quimb and Qmatchatea
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- single-node GPU or GPUs through Cutensornet and Qmatchatea
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- multi-node multi-GPU with Message Passing Interface (MPI) through Cutensornet
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- multi-node multi-GPU with NVIDIA Collective Communications Library (NCCL) through Cutensornet
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How to Use the Documentation
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============================
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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.
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1. **Getting started**: Begin by referring to the
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:doc:`/getting-started/installation/` guide to set up the ``QiboTN`` library in your environment.
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2. **Tutorials**: Explore the :doc:`getting-started/quickstart/` section for basic usage examples
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Contents
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--------
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.. toctree::
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:maxdepth: 2
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:caption: Introduction
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getting-started/index
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.. toctree::
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:maxdepth: 1
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:caption: Main documentation
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api-reference/qibotn
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Developer guides <https://qibo.science/qibo/stable/developer-guides/index.html>
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.. toctree::
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:maxdepth: 1
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:caption: Documentation links
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Qibo docs <https://qibo.science/qibo/stable/>
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Qibolab docs <https://qibo.science/qibolab/stable/>
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Qibocal docs <https://qibo.science/qibocal/stable/>
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Qibosoq docs <https://qibo.science/qibosoq/stable/>
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Qibochem docs <https://qibo.science/qibochem/stable/>
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Qibotn docs <https://qibo.science/qibotn/stable/>
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Qibo-cloud-backends docs <https://qibo.science/qibo-cloud-backends/stable/>
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Indices and tables
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==================
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* :ref:`genindex`
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* :ref:`modindex`
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* :ref:`search`
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