TensorLy-Quantum: Quantum Machine Learning with Tensor Methods
- URL: http://arxiv.org/abs/2112.10239v1
- Date: Sun, 19 Dec 2021 19:26:17 GMT
- Title: TensorLy-Quantum: Quantum Machine Learning with Tensor Methods
- Authors: Taylor L. Patti, Jean Kossaifi, Susanne F. Yelin, Anima Anandkumar
- Abstract summary: We create a Python library for quantum circuit simulation that adopts the PyTorch API.
Ly-Quantum can scale to hundreds of qubits on a single GPU and thousands of qubits on multiple GPU.
- Score: 67.29221827422164
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Simulation is essential for developing quantum hardware and algorithms.
However, simulating quantum circuits on classical hardware is challenging due
to the exponential scaling of quantum state space. While factorized tensors can
greatly reduce this overhead, tensor network-based simulators are relatively
few and often lack crucial functionalities. To address this deficiency, we
created TensorLy-Quantum, a Python library for quantum circuit simulation that
adopts the PyTorch API. Our library leverages the optimized tensor methods of
the existing TensorLy ecosystem to represent, simulate, and manipulate
large-scale quantum circuits. Through compact tensor representations and
efficient operations, TensorLy-Quantum can scale to hundreds of qubits on a
single GPU and thousands of qubits on multiple GPUs. TensorLy-Quantum is
open-source and accessible at https://github.com/tensorly/quantum
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