Yao.jl: Extensible, Efficient Framework for Quantum Algorithm Design

Xiu-Zhe Luo1,2,3,4, Jin-Guo Liu1, Pan Zhang2, and Lei Wang1,5

1Institute of Physics, Chinese Academy of Sciences, Beijing 100190, China
2Institute of Theoretical Physics, Chinese Academy of Sciences, Beijing 100190, China
3Department of Physics and Astronomy, University of Waterloo, Waterloo N2L 3G1, Canada
4Perimeter Institute for Theoretical Physics, Waterloo, Ontario N2L 2Y5, Canada
5Songshan Lake Materials Laboratory, Dongguan, Guangdong 523808, China

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We introduce $\texttt{Yao}$, an extensible, efficient open-source framework for quantum algorithm design. $\texttt{Yao}$ features generic and differentiable programming of quantum circuits. It achieves state-of-the-art performance in simulating small to intermediate-sized quantum circuits that are relevant to near-term applications. We introduce the design principles and critical techniques behind $\texttt{Yao}$. These include the quantum block intermediate representation of quantum circuits, a builtin automatic differentiation engine optimized for reversible computing, and batched quantum registers with GPU acceleration. The extensibility and efficiency of $\texttt{Yao}$ help boost innovation in quantum algorithm design.

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[1] Jakob S Kottmann, Sumner Alperin-Lea, Teresa Tamayo-Mendoza, Alba Cervera-Lierta, Cyrille Lavigne, Tzu-Ching Yen, Vladyslav Verteletskyi, Philipp Schleich, Abhinav Anand, Matthias Degroote, Skylar Chaney, Maha Kesibi, Naomi Grace Curnow, Brandon Solo, Georgios Tsilimigkounakis, Claudia Zendejas-Morales, Artur F Izmaylov, and Alán Aspuru-Guzik, "TEQUILA: a platform for rapid development of quantum algorithms", Quantum Science and Technology 6 2, 024009 (2021).

[2] Oleksandr Kyriienko, Annie E. Paine, and Vincent E. Elfving, "Solving nonlinear differential equations with differentiable quantum circuits", Physical Review A 103 5, 052416 (2021).

[3] Andrea Mari, Thomas R. Bromley, and Nathan Killoran, "Estimating the gradient and higher-order derivatives on quantum hardware", Physical Review A 103 1, 012405 (2021).

[4] Tatiana A. Bespalova and Oleksandr Kyriienko, "Hamiltonian Operator Approximation for Energy Measurement and Ground-State Preparation", PRX Quantum 2 3, 030318 (2021).

[5] Yasunari Suzuki, Yoshiaki Kawase, Yuya Masumura, Yuria Hiraga, Masahiro Nakadai, Jiabao Chen, Ken M. Nakanishi, Kosuke Mitarai, Ryosuke Imai, Shiro Tamiya, Takahiro Yamamoto, Tennin Yan, Toru Kawakubo, Yuya O. Nakagawa, Yohei Ibe, Youyuan Zhang, Hirotsugu Yamashita, Hikaru Yoshimura, Akihiro Hayashi, and Keisuke Fujii, "Qulacs: a fast and versatile quantum circuit simulator for research purpose", Quantum 5, 559 (2021).

[6] Vincent Paul Su, "Variational preparation of the thermofield double state of the Sachdev-Ye-Kitaev model", Physical Review A 104 1, 012427 (2021).

[7] Jin-Guo Liu, Lei Wang, and Pan Zhang, "Tropical Tensor Network for Ground States of Spin Glasses", Physical Review Letters 126 9, 090506 (2021).

[8] Chen Zhang, Zeyu Song, Haojie Wang, Kaiyuan Rong, and Jidong Zhai, Proceedings of the ACM International Conference on Supercomputing 443 (2021) ISBN:9781450383356.

[9] M. Cerezo, Andrew Arrasmith, Ryan Babbush, Simon C. Benjamin, Suguru Endo, Keisuke Fujii, Jarrod R. McClean, Kosuke Mitarai, Xiao Yuan, Lukasz Cincio, and Patrick J. Coles, "Variational quantum algorithms", Nature Reviews Physics 3 9, 625 (2021).

[10] Tong Liu, Jin-Guo Liu, and Heng Fan, "Probabilistic nonunitary gate in imaginary time evolution", Quantum Information Processing 20 6, 204 (2021).

[11] Tobias Haug, Kishor Bharti, and M.S. Kim, "Capacity and Quantum Geometry of Parametrized Quantum Circuits", PRX Quantum 2 4, 040309 (2021).

[12] Maria Schuld and Francesco Petruccione, Quantum Science and Technology 1 (2021) ISBN:978-3-030-83097-7.

[13] Chen Zhao and Xiao-Shan Gao, "QDNN: deep neural networks with quantum layers", Quantum Machine Intelligence 3 1, 15 (2021).

[14] Sirui Lu, Lu-Ming Duan, and Dong-Ling Deng, "Quantum adversarial machine learning", Physical Review Research 2 3, 033212 (2020).

[15] Feng Pan, Pengfei Zhou, Sujie Li, and Pan Zhang, "Contracting Arbitrary Tensor Networks: General Approximate Algorithm and Applications in Graphical Models and Quantum Circuit Simulations", Physical Review Letters 125 6, 060503 (2020).

[16] Stavros Efthymiou, Sergi Ramos-Calderer, Carlos Bravo-Prieto, Adrián Pérez-Salinas, Diego García-Martín, Artur Garcia-Saez, José Ignacio Latorre, and Stefano Carrazza, "Qibo: a framework for quantum simulation with hardware acceleration", arXiv:2009.01845.

[17] Jin-Guo Liu, Liang Mao, Pan Zhang, and Lei Wang, "Solving Quantum Statistical Mechanics with Variational Autoregressive Networks and Quantum Circuits", arXiv:1912.11381.

[18] Tyson Jones, "Efficient classical calculation of the Quantum Natural Gradient", arXiv:2011.02991.

[19] Jin-Guo Liu and Taine Zhao, "Differentiate Everything with a Reversible Embeded Domain-Specific Language", arXiv:2003.04617.

[20] Carsten Bauer, "Fast and stable determinant quantum Monte Carlo", arXiv:2003.05286.

The above citations are from Crossref's cited-by service (last updated successfully 2021-10-19 20:46:05) and SAO/NASA ADS (last updated successfully 2021-10-19 20:46:06). The list may be incomplete as not all publishers provide suitable and complete citation data.