quantum icon indicating copy to clipboard operation
quantum copied to clipboard

Tensor-Based Quantum Machine Learning

.. image:: https://badge.fury.io/py/tensorly-quantum.svg :target: https://badge.fury.io/py/tensorly-quantum

.. image:: https://github.com/tensorly/quantum/actions/workflows/test.yml/badge.svg :target: https://github.com/tensorly/quantum/actions/workflows/test.yml

.. image:: https://codecov.io/gh/tensorly/quantum/branch/main/graph/badge.svg?token=5P8GZ8YLO7 :target: https://codecov.io/gh/tensorly/quantum

================ TensorLy_Quantum

TensorLy-Quantum is a Python library for Tensor-Based Quantum Machine Learning that builds on top of TensorLy <https://github.com/tensorly/tensorly/>_ and PyTorch <https://pytorch.org/>_.

  • Website: http://tensorly.org/quantum/
  • Source-code: https://github.com/tensorly/quantum
  • If TensorLy-Quantum is useful in your research, please cite us at: https://arxiv.org/abs/2112.10239

With TensorLy-Quantum, you can easily:

  • Create large quantum circuit: Tensor network formalism requires up to exponentially less memory for quantum simulation than traditional vector and matrix approaches.
  • Leverage tensor methods: the state vectors are efficiently represented in factorized form as Tensor-Rings (MPS) and the operators as TT-Matrices (MPO)
  • Efficient simulation: tensorly-quantum leverages the factorized structure to efficiently perform quantum simulation without ever forming the full, dense operators and state-vectors
  • Multi-Basis Encoding: we provide multi-basis encoding out-of-the-box for scalable experimentation
  • Solve hard problems: we provide all the tools to solve the MaxCut problem for an unprecendented number of qubits / vertices

Installing TensorLy-Quantum

Through pip

.. code::

pip install tensorly-quantum

From source

.. code::

git clone https://github.com/tensorly/quantum cd quantum pip install -e .