TensorCircuit: a Quantum Software Framework for the NISQ Era

Shi-Xin Zhang1, Jonathan Allcock2, Zhou-Quan Wan1,3, Shuo Liu1,3, Jiace Sun4, Hao Yu5, Xing-Han Yang1,6, Jiezhong Qiu1, Zhaofeng Ye1, Yu-Qin Chen1, Chee-Kong Lee7, Yi-Cong Zheng1, Shao-Kai Jian8, Hong Yao3, Chang-Yu Hsieh1, and Shengyu Zhang1

1Tencent Quantum Laboratory, Tencent, Shenzhen, Guangdong 518057, China
2Tencent Quantum Laboratory, Tencent, Hong Kong, China
3Institute for Advanced Study, Tsinghua University, Beijing 100084, China
4Division of Chemistry and Chemical Engineering, California Institute of Technology, Pasadena, CA 91125, USA
5Department of Electrical and Computer Engineering, McGill University, Quebec H3A 0E9 , Canada
6Shenzhen Middle School, Shenzhen, Guangdong 518025, China
7Tencent America, Palo Alto, California 94306, USA
8Department of Physics, Brandeis University, Waltham, Massachusetts 02453, USA

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Abstract

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.

In this paper, we introduce TensorCircuit: a Quantum Software Framework for the NISQ Era.

TensorCircuit is an open source quantum simulation framework in Python designed for speed, flexibility and elegance. The simulation is powered by an advanced tensor network engine and is implemented with the popular TensorFlow, JAX, and PyTorch machine learning frameworks in a backend agnostic way. TensorCircuit is compatible with modern machine learning engineering paradigms — automatic differentiation, just-in-time compilation, vectorized parallelism and GPU acceleration — which make it especially suited to simulating variational algorithms based on parameterized quantum circuits.

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[5] Weitang Li, Jonathan Allcock, Lixue Cheng, Shi-Xin Zhang, Yu-Qin Chen, Jonathan P. Mailoa, Zhigang Shuai, and Shengyu Zhang, "TenCirChem: An Efficient Quantum Computational Chemistry Package for the NISQ Era", Journal of Chemical Theory and Computation 19 13, 3966 (2023).

[6] Weitang Li, Jiajun Ren, Sainan Huai, Tianqi Cai, Zhigang Shuai, and Shengyu Zhang, "Efficient quantum simulation of electron-phonon systems by variational basis state encoder", Physical Review Research 5 2, 023046 (2023).

[7] Tailong Xiao, Xinliang Zhai, Xiaoyan Wu, Jianping Fan, and Guihua Zeng, "Practical advantage of quantum machine learning in ghost imaging", Communications Physics 6 1, 171 (2023).

[8] Shuo Liu, Shi-Xin Zhang, Shao-Kai Jian, and Hong Yao, "Training variational quantum algorithms with random gate activation", Physical Review Research 5 3, L032040 (2023).

[9] Shuo Liu, Shi-Xin Zhang, Chang-Yu Hsieh, Shengyu Zhang, and Hong Yao, "Probing many-body localization by excited-state variational quantum eigensolver", Physical Review B 107 2, 024204 (2023).

[10] He-Liang Huang, Xiao-Yue Xu, Chu Guo, Guojing Tian, Shi-Jie Wei, Xiaoming Sun, Wan-Su Bao, and Gui-Lu Long, "Near-term quantum computing techniques: Variational quantum algorithms, error mitigation, circuit compilation, benchmarking and classical simulation", Science China Physics, Mechanics, and Astronomy 66 5, 250302 (2023).

[11] Marcel Niedermeier, Jose L. Lado, and Christian Flindt, "Tensor-Network Simulations of Noisy Quantum Computers", arXiv:2304.01751, (2023).

[12] Bingzhi Zhang, Peng Xu, Xiaohui Chen, and Quntao Zhuang, "Generative Quantum Machine Learning via Denoising Diffusion Probabilistic Models", Physical Review Letters 132 10, 100602 (2024).

[13] Jiaqi Miao, Chang-Yu Hsieh, and Shi-Xin Zhang, "Neural-network-encoded variational quantum algorithms", Physical Review Applied 21 1, 014053 (2024).

[14] Amit Jamadagni, Andreas M. Läuchli, and Cornelius Hempel, "Benchmarking quantum computer simulation software packages", arXiv:2401.09076, (2024).

[15] Chee Kong Lee, Shi-Xin Zhang, Chang-Yu Hsieh, Shengyu Zhang, and Liang Shi, "Variational Quantum Simulations of Finite-Temperature Dynamical Properties via Thermofield Dynamics", arXiv:2206.05571, (2022).

[16] Elies Gil-Fuster, Jens Eisert, and Carlos Bravo-Prieto, "Understanding quantum machine learning also requires rethinking generalization", Nature Communications 15, 2277 (2024).

[17] Zhimin He, Maijie Deng, Shenggen Zheng, Lvzhou Li, and Haozhen Situ, "GSQAS: Graph Self-supervised Quantum Architecture Search", Physica A Statistical Mechanics and its Applications 630, 129286 (2023).

[18] Shi-Xin Zhang, Zhou-Quan Wan, Chang-Yu Hsieh, Hong Yao, and Shengyu Zhang, "Variational Quantum-Neural Hybrid Error Mitigation", arXiv:2112.10380, (2021).

[19] Yu-Cheng Chen, Yu-Qin Chen, Alice Hu, Chang-Yu Hsieh, and Shengyu Zhang, "Variational quantum simulation of the imaginary-time Lyapunov control for accelerating the ground-state preparation", arXiv:2112.11782, (2021).

[20] Yu-Qin Chen, Shi-Xin Zhang, and Shengyu Zhang, "Non-Markovianity Benefits Quantum Dynamics Simulation", arXiv:2311.17622, (2023).

[21] Haimeng Zhao, "Non-IID Quantum Federated Learning with One-shot Communication Complexity", arXiv:2209.00768, (2022).

[22] Zhiwen Zong, Sainan Huai, Tianqi Cai, Wenyan Jin, Ze Zhan, Zhenxing Zhang, Kunliang Bu, Liyang Sui, Ying Fei, Yicong Zheng, Shengyu Zhang, Jianlan Wu, and Yi Yin, "Determination of molecular energies via variational-based quantum imaginary time evolution in a superconducting qubit system", Science China Physics, Mechanics, and Astronomy 67 4, 240311 (2024).

[23] Lixue Cheng, Yu-Qin Chen, Shi-Xin Zhang, and Shengyu Zhang, "Quantum approximate optimization via learning-based adaptive optimization", Communications Physics 7 1, 83 (2024).

[24] Tailong Xiao, Jingzheng Huang, Hongjing Li, Jianping Fan, and Guihua Zeng, "Quantum generative adversarial imitation learning", New Journal of Physics 25 3, 033034 (2023).

[25] Dev Gurung, Shiva Raj Pokhrel, and Gang Li, "Decentralized Quantum Federated Learning for Metaverse: Analysis, Design and Implementation", arXiv:2306.11297, (2023).

[26] Zhihui Song, Jinchen Xu, Xin Zhou, Xiaodong Ding, and Zheng Shan, "Transforming two-dimensional tensor networks into quantum circuits for supervised learning", Machine Learning: Science and Technology 5 1, 015048 (2024).

The above citations are from Crossref's cited-by service (last updated successfully 2024-01-12 09:15:14) and SAO/NASA ADS (last updated successfully 2024-04-15 03:49:39). The list may be incomplete as not all publishers provide suitable and complete citation data.

Could not fetch Crossref cited-by data during last attempt 2024-04-15 03:49:36: Encountered the unhandled forward link type postedcontent_cite while looking for citations to DOI 10.22331/q-2023-02-02-912.