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image:: https://badge.fury.io/py/tensorly-quantum.svg\n    :target: https://badge.fury.io/py/tensorly-quantum\n\n.. image:: https://github.com/tensorly/quantum/actions/workflows/test.yml/badge.svg\n    :target: https://github.com/tensorly/quantum/actions/workflows/test.yml\n\n.. image:: https://codecov.io/gh/tensorly/quantum/branch/main/graph/badge.svg?token=5P8GZ8YLO7\n    :target: https://codecov.io/gh/tensorly/quantum\n    \n\n================\nTensorLy_Quantum\n================\n\n\nTensorLy-Quantum is a Python library for Tensor-Based Quantum Machine Learning that\nbuilds on top of `TensorLy \u003chttps://github.com/tensorly/tensorly/\u003e`_\nand `PyTorch \u003chttps://pytorch.org/\u003e`_.\n\n- **Website:** http://tensorly.org/quantum/\n- **Source-code:**  https://github.com/tensorly/quantum\n- **If TensorLy-Quantum is useful in your research, please cite us at:**  https://arxiv.org/abs/2112.10239\n\nWith TensorLy-Quantum, you can easily: \n\n- **Create large quantum circuit**: Tensor network formalism requires up to exponentially less memory for quantum simulation than traditional vector and matrix approaches.\n- **Leverage tensor methods**: the state vectors are efficiently represented in factorized form as Tensor-Rings (MPS) and the operators as TT-Matrices (MPO)\n- **Efficient simulation**: tensorly-quantum leverages the factorized structure to efficiently perform quantum simulation without ever forming the full, dense operators and state-vectors\n- **Multi-Basis Encoding**: we provide multi-basis encoding out-of-the-box for scalable experimentation\n- **Solve hard problems**: we provide all the tools to solve the MaxCut problem for an unprecendented number of qubits / vertices\n\n\nInstalling TensorLy-Quantum\n============================\n\nThrough pip\n-----------\n\n.. code:: \n\n   pip install tensorly-quantum\n   \n   \nFrom source\n-----------\n\n.. code::\n\n  git clone https://github.com/tensorly/quantum\n  cd quantum\n  pip install -e .\n  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