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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► References
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[22] Jakob S. Kottmann and Alán Aspuru-Guzik, “Optimized Low-Depth Quantum Circuits for Molecular Electronic Structure using a Separable Pair Approximation”, arXiv:2105.03836.
[23] Nicholas H. Stair and Francesco A. Evangelista, “QForte: an efficient state simulator and quantum algorithms library for molecular electronic structure”, arXiv:2108.04413.
[24] 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.
[25] Hrushikesh Patil, Yulun Wang, and Predrag Krstic, “Variational Quantum Linear Solver with Dynamic Ansatz”, arXiv:2107.08606.
[26] Kosuke Ito, Wataru Mizukami, and Keisuke Fujii, “Universal noise-precision relations in variational quantum algorithms”, arXiv:2106.03390.
[27] Oumarou Oumarou, Alexandru Paler, and Robert Basmadjian, “Fast quantum circuit simulation using hardware accelerated general purpose libraries”, arXiv:2106.13995.
[28] Bingzhi Zhang and Quntao Zhuang, “Fast suppression of classification error in variational quantum circuits”, arXiv:2107.08026.
[29] Kouhei Nakaji, Hiroyuki Tezuka, and Naoki Yamamoto, “Quantum-enhanced neural networks in the neural tangent kernel framework”, arXiv:2109.03786.
[30] William M Watkins, Samuel Yen-Chi Chen, and Shinjae Yoo, “Quantum machine learning with differential privacy”, arXiv:2103.06232.
[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.
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The above citations are from SAO/NASA ADS (last updated successfully 2021-10-06 10:04:51). The list may be incomplete as not all publishers provide suitable and complete citation data.
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This Paper is published in Quantum under the Creative Commons Attribution 4.0 International (CC BY 4.0) license. Copyright remains with the original copyright holders such as the authors or their institutions.
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Source: https://quantum-journal.org/papers/q-2021-10-06-559/
