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cuTensorNet: A High-Performance Library for Tensor Network Computations
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Welcome to the cuTensorNet library documentation!

**NVIDIA cuTensorNet** is a high-performance library for tensor network computations,
a component of the :doc:`NVIDIA cuQuantum SDK <../index>`. Functionalities of *cuTensorNet* are described in 
:doc:`Overview <overview>` with installation and usage guide provided in :doc:`Getting Started <../getting-started/index>`. 

..
  TODO: mention what cuTensorNet can be used in addition to quantum circuit simulations.
  We don't want to limit ourselves to quantum information science. Tensor networks can
  be used in many areas beyond that.


.. topic:: Key Features

   * Based on NVIDIA's high-performance tensor algebra library: `cuTENSOR <https://developer.nvidia.com/cutensor>`_
   * Provides APIs for:

     - Creating a tensor or tensor network object
     - Finding a cost-optimal tensor network contraction path for any given tensor network
     - Finding a low-overhead slicing for the tensor network contraction to meet specified memory constraints
     - Tuning the tensor network contraction path finder configuration for better performance
     - Performing tensor network contraction plan generation, auto-tuning, and its subsequent execution
     - Gradually constructing a tensor network state (e.g., a quantum circuit state), followed by
       computing its properties, including arbitrary slices of amplitudes, expectation values,
       marginal distributions (reduced density matrices), as well as performing direct sampling
     - Performing backward propagation to compute gradients of the output tensor w.r.t. user-specified input tensors
     - Performing tensor decomposition using QR or SVD
     - Applying a quantum gate operand to a pair of connected (contracted) tensors
     - Enabling automatic distributed parallelization in the contraction path finder and executor
     - Enabling custom memory management
     - Logging


.. topic:: Support

   * *Supported GPU Architectures*: ``Volta``, ``Ampere``, ``Hopper``
   * *Supported OS*: ``Linux``
   * *Supported CPU Architectures*: ``x86_64``, ``ARM64``, ``ppc64le``


.. topic:: Prerequisites

   * `CUDA® Toolkit 11.x <https://developer.nvidia.com/cuda-downloads>`_ and compatible driver r450+ (see `CUDA Toolkit Release Notes <https://docs.nvidia.com/cuda/cuda-toolkit-release-notes/index.html#cuda-major-component-versions>`_).
   * `cuTENSOR v2.0.1 <https://developer.nvidia.com/cutensor>`_


.. toctree::
   :caption: Contents
   :maxdepth: 2

   release-notes
   overview
   examples
   api/index
   acknowledgements