TensorLy-Quantum: Quantum Machine Learning with Tensor Methods

TL;DR

TensorLy-Quantum is a Python library that uses tensor methods to efficiently simulate large-scale quantum circuits on classical hardware, enabling scalable quantum computing research.

Contribution

It introduces a tensor network-based quantum circuit simulator integrated with the TensorLy ecosystem, supporting large-scale simulations on GPUs.

Findings

Scales to hundreds of qubits on a single GPU

Supports thousands of qubits on multiple GPUs

Open-source and compatible with PyTorch

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