Qulacs: a fast and versatile quantum circuit simulator for research purpose

Yasunari Suzuki1,2, Yoshiaki Kawase3, Yuya Masumura4, Yuria Hiraga5, Masahiro Nakadai6, Jiabao Chen7, Ken M. Nakanishi7,8, Kosuke Mitarai3,7,9, Ryosuke Imai7, Shiro Tamiya7,10, Takahiro Yamamoto7, Tennin Yan7, Toru Kawakubo7, Yuya O. Nakagawa7, Yohei Ibe7, Youyuan Zhang7,8, Hirotsugu Yamashita11, Hikaru Yoshimura11, Akihiro Hayashi12, and Keisuke Fujii2,3,9,13

1NTT Computer and Data Science Laboratories, NTT Corporation, Musashino 180-8585, Japan
2JST PRESTO, Kawaguchi, Saitama 332-0012, Japan
3Graduate School of Engineering Science, Osaka University, 1-3 Machikaneyama, Toyonaka, Osaka 560-8531, Japan
4Graduate School of Information Science and Technology, Osaka University, 1-1 Yamadaoka, Suita, Osaka 565-0871, Japan
5Graduate School of Information and Science, Nara Institute of Science and Technology, Takayama, Ikoma, Nara 630-0192, Japan
6Graduate School of Science, Kyoto University, Yoshida-Ushinomiya, Sakyo, Kyoto 606-8302, Japan
7QunaSys Inc., Aqua Hakusan Building 9F, 1-13-7 Hakusan, Bunkyo, Tokyo 113-0001, Japan
8Graduate School of Science, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-0033, Japan
9Center for Quantum Information and Quantum Biology, Institute for Open and Transdisciplinary Research Initiatives, Osaka University, Japan
10Graduate School of Engineering, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-0033, Japan
11Individual researcher
12School of Computer Science, Georgia Institute of Technology, Atlanta, GA, 30332, USA
13Center for Emergent Matter Science, RIKEN, Wako Saitama 351-0198, Japan

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Abstract

To explore the possibilities of a near-term intermediate-scale quantum algorithm and long-term fault-tolerant quantum computing, a fast and versatile quantum circuit simulator is needed. Here, we introduce Qulacs, a fast simulator for quantum circuits intended for research purpose. We show the main concepts of Qulacs, explain how to use its features via examples, describe numerical techniques to speed-up simulation, and demonstrate its performance with numerical benchmarks.

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[12] Samuel Yen-Chi Chen, Tzu-Chieh Wei, Chao Zhang, Haiwang Yu, and Shinjae Yoo, "Hybrid Quantum-Classical Graph Convolutional Network", arXiv:2101.06189.

[13] Jakob S. Kottmann, Philipp Schleich, Teresa Tamayo-Mendoza, and Alán Aspuru-Guzik, "Reducing qubit requirements while maintaining numerical precision for the Variational Quantum Eigensolver: A Basis-Set-Free Approach", arXiv:2008.02819.

[14] Nobuyuki Yoshioka, Hideaki Hakoshima, Yuichiro Matsuzaki, Yuuki Tokunaga, Yasunari Suzuki, and Suguru Endo, "Generalized quantum subspace expansion", arXiv:2107.02611.

[15] Kosuke Mitarai, Yasunari Suzuki, Wataru Mizukami, Yuya O. Nakagawa, and Keisuke Fujii, "Quadratic Clifford expansion for efficient benchmarking and initialization of variational quantum algorithms", arXiv:2011.09927.

[16] Takeru Kusumoto, Kosuke Mitarai, Keisuke Fujii, Masahiro Kitagawa, and Makoto Negoro, "Experimental quantum kernel trick with nuclear spins in a solid", npj Quantum Information 7, 94 (2021).

[17] Samuel Yen-Chi Chen and Shinjae Yoo, "Federated Quantum Machine Learning", arXiv:2103.12010.

[18] Keita Arimitsu, Yuya O. Nakagawa, Sho Koh, Wataru Mizukami, Qi Gao, and Takao Kobayashi, "Analytic energy gradient for state-averaged orbital-optimized variational quantum eigensolvers and its application to a photochemical reaction", arXiv:2107.12705.

[19] Nicholas H. Stair and Francesco A. Evangelista, "QForte: an efficient state simulator and quantum algorithms library for molecular electronic structure", arXiv:2108.04413.

[20] Shiro Tamiya, Sho Koh, and Yuya O. Nakagawa, "Calculating nonadiabatic couplings and Berry's phase by variational quantum eigensolvers", Physical Review Research 3 2, 023244 (2021).

[21] Jakob S. Kottmann and Alán Aspuru-Guzik, "Optimized Low-Depth Quantum Circuits for Molecular Electronic Structure using a Separable Pair Approximation", arXiv:2105.03836.

[22] Nicholas C. Rubin, Klaas Gunst, Alec White, Leon Freitag, Kyle Throssell, Garnet Kin-Lic Chan, Ryan Babbush, and Toru Shiozaki, "The Fermionic Quantum Emulator", arXiv:2104.13944.

[23] Samuel Yen-Chi Chen, Chih-Min Huang, Chia-Wei Hsing, and Ying-Jer Kao, "An end-to-end trainable hybrid classical-quantum classifier", arXiv:2102.02416.

[24] Hans Hon Sang Chan, Nathan Fitzpatrick, Javier Segarra-Marti, Michael J. Bearpark, and David P. Tew, "Molecular Excited State Calculations with Adaptive Wavefunctions on a Quantum Eigensolver Emulation: Reducing Circuit Depth and Separating Spin States", arXiv:2105.10275.

[25] Kentaro Yamamoto, David Zsolt Manrique, Irfan Khan, Hideaki Sawada, and David Muñoz Ramo, "Quantum hardware calculations of periodic systems: hydrogen chain and iron crystals", arXiv:2109.08401.

[26] Kosuke Ito, Wataru Mizukami, and Keisuke Fujii, "Universal noise-precision relations in variational quantum algorithms", arXiv:2106.03390.

[27] Cenk Tüysüz, Carla Rieger, Kristiane Novotny, Bilge Demirköz, Daniel Dobos, Karolos Potamianos, Sofia Vallecorsa, Jean-Roch Vlimant, and Richard Forster, "Hybrid Quantum Classical Graph Neural Networks for Particle Track Reconstruction", arXiv:2109.12636.

[28] Oumarou Oumarou, Alexandru Paler, and Robert Basmadjian, "Fast quantum circuit simulation using hardware accelerated general purpose libraries", arXiv:2106.13995.

[29] Hrushikesh Patil, Yulun Wang, and Predrag Krstic, "Variational Quantum Linear Solver with Dynamic Ansatz", arXiv:2107.08606.

[30] Bingzhi Zhang and Quntao Zhuang, "Fast suppression of classification error in variational quantum circuits", arXiv:2107.08026.

[31] Maria-Andreea Filip, Nathan Fitzpatrick, David Muñoz Ramo, and Alex J. W. Thom, "The Best of Both Worlds: Optimizing Quantum Hardware Resources with Classical Stochastic Methods", arXiv:2108.10912.

[32] William M Watkins, Samuel Yen-Chi Chen, and Shinjae Yoo, "Quantum machine learning with differential privacy", arXiv:2103.06232.

[33] Kouhei Nakaji, Hiroyuki Tezuka, and Naoki Yamamoto, "Quantum-enhanced neural networks in the neural tangent kernel framework", arXiv:2109.03786.

[34] Maurice Weber, Abhinav Anand, Alba Cervera-Lierta, Jakob S. Kottmann, Thi Ha Kyaw, Bo Li, Alán Aspuru-Guzik, Ce Zhang, and Zhikuan Zhao, "Toward Reliability in the NISQ Era: Robust Interval Guarantee for Quantum Measurements on Approximate States", arXiv:2110.09793.

[35] Bingzhi Zhang, Akira Sone, and Quntao Zhuang, "Computational phase transition in Quantum Approximate Optimization Algorithm -- the difference between hard and easy", arXiv:2109.13346.

The above citations are from SAO/NASA ADS (last updated successfully 2021-10-27 12:02:08). The list may be incomplete as not all publishers provide suitable and complete citation data.

On Crossref's cited-by service no data on citing works was found (last attempt 2021-10-27 12:02:06).