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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Abstract
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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Cited by
[1] 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).
[2] Jin-Guo Liu, Liang Mao, Pan Zhang, and Lei Wang, “Solving Quantum Statistical Mechanics with Variational Autoregressive Networks and Quantum Circuits”, arXiv:1912.11381.
[3] Sirui Lu, Lu-Ming Duan, and Dong-Ling Deng, “Quantum adversarial machine learning”, Physical Review Research 2 3, 033212 (2020).
[4] Tatiana A. Bespalova and Oleksandr Kyriienko, “Hamiltonian operator approximation for energy measurement and ground state preparation”, arXiv:2009.03351.
[5] Tong Liu, Jin-Guo Liu, and Heng Fan, “Probabilistic Nonunitary Gate in Imaginary Time Evolution”, arXiv:2006.09726.
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[7] Jin-Guo Liu and Taine Zhao, “Differentiate Everything with a Reversible Domain-Specific Language”, arXiv:2003.04617.
[8] Carsten Bauer, “Fast and stable determinant quantum Monte Carlo”, arXiv:2003.05286.
[9] Chen Zhao and Xiao-Shan Gao, “QDNN: DNN with Quantum Neural Network Layers”, arXiv:1912.12660.
[10] The Quingo Development Team, “Quingo: A Programming Framework for Heterogeneous Quantum-Classical Computing with NISQ Features”, arXiv:2009.01686.
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The above citations are from SAO/NASA ADS (last updated successfully 2020-10-16 04:52:30). 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 2020-10-16 04:52:29).
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Source: https://quantum-journal.org/papers/q-2020-10-11-341/
