TensorLy-Quantum: Quantum Machine Learning with Tensor Methods
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 rela...
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Main Authors | , , , |
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Format | Journal Article |
Language | English |
Published |
19.12.2021
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Subjects | |
Online Access | Get full text |
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Summary: | 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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DOI: | 10.48550/arxiv.2112.10239 |