TensorCircuit-NG | SXZ group

Introduction

TensorCircuit-NG is a high-performance, open-source quantum software framework designed to power the next generation of quantum computing research and development. Engineered to overcome limitations in existing platforms, TensorCircuit-NG features a sophisticated tensor network engine that delivers unparalleled simulation speed and scalability. Built with seamless integration with leading machine learning frameworks like TensorFlow, JAX, and PyTorch, it provides a versatile and efficient environment for exploring both quantum algorithms and hybrid quantum-classical approaches.

At its core, TensorCircuit-NG offers significant performance advantages, achieving speedups of up to 10^6 compared to other major software on common tasks and enabling the simulation of 100-1000 qubits for specific structures. Its deep integration with the ML ecosystem facilitates advanced features like automatic differentiation, GPU/TPU acceleration, and the flexible composition of quantum circuits, tensor networks, and neural networks. TensorCircuit-NG's universality covers a wide range of simulation scenarios, from exact and noisy circuit simulation to more specialized areas like fermionic or qudit systems.

TensorCircuit-NG has rapidly become a foundational tool for quantum computing worldwide. With over 800,000 downloads and cited in more than 100 research publications, its user base spans prestigious global institutions including Harvard, MIT, Caltech, Tsinghua, and Peking University, alongside industry leaders like NVIDIA, IBM, and Google. Its adoption by top labs underscores its recognized excellence. TensorCircuit-NG's impact is further highlighted by its selection as a top10 quantum industry event in QuantumChina and its unique inclusion in NVIDIA cuQuantum performance benchmarks.

The whitepaper

The whitepaper for TensorCircuit of v0.x: (Zhang et al., 2023)

History of the package

See here for the brief history of TensorCircuit.

$refraction-ray/tensorcircuit$ $refraction-ray/tensorcircuit$

References

2023

Quantum
TensorCircuit: a Quantum Software Framework for the NISQ Era

Shi-Xin Zhang, Jonathan Allcock, Zhou-Quan Wan, Shuo Liu, Jiace Sun, Hao Yu, and 10 more authors

Quantum, Feb 2023

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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.
@article{Zhang2022_tc, archiveprefix = {arXiv}, arxivid = {2205.10091}, author = {Zhang, Shi-Xin and Allcock, Jonathan and Wan, Zhou-Quan and Liu, Shuo and Sun, Jiace and Yu, Hao and Yang, Xing-Han and Qiu, Jiezhong and Ye, Zhaofeng and Chen, Yu-Qin and Lee, Chee-Kong and Zheng, Yi-Cong and Jian, Shao-Kai and Yao, Hong and Hsieh, Chang-Yu and Zhang, Shengyu}, doi = {10.22331/q-2023-02-02-912}, eprint = {2205.10091}, issn = {2521-327X}, journal = {Quantum}, month = feb, pages = {912}, title = {{TensorCircuit: a Quantum Software Framework for the NISQ Era}}, url = {https://quantum-journal.org/papers/q-2023-02-02-912/}, volume = {7}, year = {2023}, }

Introduction

Links

The whitepaper

History of the package

References

2023