TensorCircuit: a Quantum Software Framework for the NISQ Era

• DOI: 10.22331/q-2023-02-02-912
• Publication Date: 2023-02-02T16:16:57Z
• Authors: Shi-Xin Zhang; Jonathan Allcock; Zhou-Quan Wan; Shuo Liu; Jiace Sun; Hao Yu; Xing-Han Yang; Jiezhong Qiu; Zhaofeng Ye; Yu-Qin Chen; Chee-Kong Lee; Yi-Cong Zheng; Shao-Kai Jian; Hong Yao; Chang-Yu Hsieh; Shengyu Zhang
• Keywords: Quantum Physics; Physics and Astronomy (miscellaneous); Physics; QC1-999; FOS: Physical sciences; Computational Physics (physics.comp-ph); 530; 01 natural sciences; 004; Atomic and Molecular Physics; 0103 physical sciences; and Optics; Quantum Physics (quant-ph); Physics - Computational Physics

TensorCircuit is an open source quantum circuit simulator based on tensor network contraction, designed for speed, flexibility and code efficiency. Written purely in Python, and built on top of industry-standard machine learning frameworks, TensorCircuit supports automatic differentiation, just-in-time compilation, vectorized parallelism and hardware acceleration. These features allow TensorCircuit to simulate larger and more complex quantum circuits than existing simulators, and are especially suited to variational algorithms based on parameterized quantum circuits. TensorCircuit enables orders of magnitude speedup for various quantum simulation tasks compared to other common quantum software, and can simulate up to 600 qubits with moderate circuit depth and low-dimensional connectivity. With its time and space efficiency, flexible and extensible architecture and compact, user-friendly API, TensorCircuit has been built to facilitate the design, simulation and analysis of quantum algorithms in the Noisy Intermediate-Scale Quantum (NISQ) era.