UniQ: a unified programming model for efficient quantum circuit simulation
Article No.: 49, Pages 1 - 16
Abstract
Quantum circuit simulation is critical for verifying quantum computers. Given exponential complexity in the simulation, existing simulators use different architectures to accelerate the simulation. However, due to the variety of both simulation methods and modern architectures, it is challenging to design a high-performance yet portable simulator.
In this work, we propose UniQ, a unified programming model for multiple simulation methods on various hardware architectures. We provide a unified application abstraction to describe different applications, and a unified hierarchical hardware abstraction upon different hardware. Based on these abstractions, UniQ can perform various circuit transformations without being aware of either concrete application or architecture detail, and generate high-performance execution schedules on different platforms without much human effort. Evaluations on CPU, GPU, and Sunway platforms show that UniQ can accelerate quantum circuit simulation by up to 28.59× (4.47× on average) over state-of-the-art frameworks, and successfully scale to 399,360 cores on 1,024 nodes.
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References
[1]
P. W. Shor, "Algorithms for quantum computation: discrete logarithms and factoring," in Proceedings 35th annual symposium on foundations of computer science. Ieee, 1994, pp. 124--134.
[2]
C. H. Bennett and G. Brassard, "Quantum cryptography: Public key distribution and coin tossing," arXiv preprint arXiv:2003.06557, 2020.
[3]
S. Lloyd, M. Mohseni, and P. Rebentrost, "Quantum algorithms for supervised and unsupervised machine learning," arXiv preprint arXiv:1307.0411, 2013.
[4]
J. Biamonte, P. Wittek, N. Pancotti, P. Rebentrost, N. Wiebe, and S. Lloyd, "Quantum machine learning," Nature, vol. 549, no. 7671, pp. 195--202, 2017.
[5]
M. Schuld and N. Killoran, "Quantum machine learning in feature hilbert spaces," Physical review letters, vol. 122, no. 4, p. 040504, 2019.
[6]
"A preview of bristlecone, google's new quantum processor." [Online]. Available: http://ai.googleblog.com/2018/03/a-preview-of-bristlecone-googles-new.html
[7]
H.-S. Zhong, H. Wang, Y.-H. Deng, M.-C. Chen, L.-C. Peng, Y.-H. Luo, J. Qin, D. Wu, X. Ding, Y. Hu et al., "Quantum computational advantage using photons," Science, vol. 370, no. 6523, pp. 1460--1463, 2020.
[8]
"A preview of bristlecone, google's new quantum processor." [Online]. Available: https://newsroom.ibm.com/2021-11-16-IBM-Unveils-Breakthrough-127-Qubit-Quantum-Processor
[9]
T. Häner and D. S. Steiger, "0.5 petabyte simulation of a 45-qubit quantum circuit," in Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis, 2017, pp. 1--10.
[10]
C. Zhang, Z. Song, H. Wang, K. Rong, and J. Zhai, "Hyquas: hybrid partitioner based quantum circuit simulation system on gpu," in Proceedings of the ACM International Conference on Supercomputing, 2021, pp. 443--454.
[11]
J. Preskill, "Quantum computing in the nisq era and beyond," Quantum, vol. 2, p. 79, 2018.
[12]
L. Xie, J. Zhai, and W. Zheng, "Mitigating crosstalk in quantum computers through commutativity-based instruction reordering," in 2021 58th ACM/IEEE Design Automation Conference (DAC), 2021, pp. 445--450.
[13]
L. Lao, P. Murali, M. Martonosi, and D. Browne, "Designing calibration and expressivity-efficient instruction sets for quantum computing," in 2021 ACM/IEEE 48th Annual International Symposium on Computer Architecture (ISCA), 2021, pp. 846--859.
[14]
T. E. O'Brien, B. Tarasinski, and L. DiCarlo, "Density-matrix simulation of small surface codes under current and projected experimental noise," npj Quantum Information, vol. 3, no. 1, p. 39, Sep 2017. [Online].
[15]
H. A. et al., "Qiskit: An open-source framework for quantum computing," 2019.
[16]
C. Developers, "Cirq," Aug. 2021, See full list of authors on Github: https://github.com/quantumlib/Cirq/graphs/contributors. [Online].
[17]
A. Li, O. Subasi, X. Yang, and S. Krishnamoorthy, "Density matrix quantum circuit simulation via the bsp machine on modern gpu clusters," in SC20: International Conference for High Performance Computing, Networking, Storage and Analysis. IEEE, 2020, pp. 1--15.
[18]
A. Li, B. Fang, C. Granade, G. Prawiroatmodjo, B. Heim, M. Roetteler, and S. Krishnamoorthy, "Sv-sim: scalable pgas-based state vector simulation of quantum circuits," in Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis, 2021, pp. 1--14.
[19]
X.-Z. Luo, J.-G. Liu, P. Zhang, and L. Wang, "Yao. jl: Extensible, efficient framework for quantum algorithm design," Quantum, vol. 4, p. 341, 2020.
[20]
T. Jones, A. Brown, I. Bush, and S. C. Benjamin, "Quest and high performance simulation of quantum computers," Scientific reports, vol. 9, no. 1, pp. 1--11, 2019.
[21]
P. Zhang, J. Yuan, and X. Lu, "Quantum computer simulation on multi-gpu incorporating data locality," in International Conference on Algorithms and Architectures for Parallel Processing. Springer, 2015, pp. 241--256.
[22]
E. Gutierrez, S. Romero, M. A. Trenas, and E. L. Zapata, "Simulation of quantum gates on a novel gpu architecture," in International Conference on Systems Theory and Scientific Computation. Citeseer, 2007.
[23]
A. Amariutei and S. Caraiman, "Parallel quantum computer simulation on the gpu," in 15th International Conference on System Theory, Control and Computing. IEEE, 2011, pp. 1--6.
[24]
Y. Suzuki, Y. Kawase, Y. Masumura, Y. Hiraga, M. Nakadai, J. Chen, K. M. Nakanishi, K. Mitarai, R. Imai, S. Tamiya, T. Yamamoto, T. Yan, T. Kawakubo, Y. O. Nakagawa, Y. Ibe, Y. Zhang, H. Yamashita, H. Yoshimura, A. Hayashi, and K. Fujii, "Qulacs: a fast and versatile quantum circuit simulator for research purpose," 2020.
[25]
J. Doi, H. Takahashi, R. Raymond, T. Imamichi, and H. Horii, "Quantum computing simulator on a heterogenous hpc system," in Proceedings of the 16th ACM International Conference on Computing Frontiers, 2019, pp. 85--93.
[26]
X.-C. Wu, S. Di, E. M. Dasgupta, F. Cappello, H. Finkel, Y. Alexeev, and F. T. Chong, "Full-state quantum circuit simulation by using data compression," in Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis, 2019, pp. 1--24.
[27]
B. Villalonga, S. Boixo, B. Nelson, C. Henze, E. Rieffel, R. Biswas, and S. Mandra, "A flexible high-performance simulator for the verification and benchmarking of quantum circuits implemented on real hardware," 2018.
[28]
B. Villalonga, D. Lyakh, S. Boixo, H. Neven, T. S. Humble, R. Biswas, E. G. Rieffel, A. Ho, and S. Mandra', "Establishing the quantum supremacy frontier with a 281 pflop/s simulation," Quantum Science and Technology, vol. 5, no. 3, p. 034003, 2020.
[29]
C. Guo, Y. Liu, M. Xiong, S. Xue, X. Fu, A. Huang, X. Qiang, P. Xu, J. Liu, S. Zheng et al., "General-purpose quantum circuit simulator with projected entangled-pair states and the quantum supremacy frontier," Physical review letters, vol. 123, no. 19, p. 190501, 2019.
[30]
I. L. Markov and Y. Shi, "Simulating quantum computation by contracting tensor networks," SIAM Journal on Computing, vol. 38, no. 3, pp. 963--981, 2008.
[31]
E. Pednault, J. A. Gunnels, G. Nannicini, L. Horesh, T. Magerlein, E. Solomonik, and R. Wisnieff, "Breaking the 49-qubit barrier in the simulation of quantum circuits," arXiv preprint arXiv:1710.05867, vol. 15, 2017.
[32]
R. Li, B. Wu, M. Ying, X. Sun, and G. Yang, "Quantum supremacy circuit simulation on sunway taihulight," IEEE Transactions on Parallel and Distributed Systems, vol. 31, no. 4, pp. 805--816, 2019.
[33]
Y. Liu, X. Liu, F. Li, H. Fu, Y. Yang, J. Song, P. Zhao, Z. Wang, D. Peng, H. Chen et al., "Closing the" quantum supremacy" gap: achieving realtime simulation of a random quantum circuit using a new sunway supercomputer," in Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis, 2021, pp. 1--12.
[34]
F. Li, X. Liu, Y. Liu, P. Zhao, Y. Yang, H. Shang, W. Sun, Z. Wang, E. Dong, and D. Chen, "Sw qsim: a minimize-memory quantum simulator with high-performance on a new sunway supercomputer," in Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis, 2021, pp. 1--13.
[35]
P. Springer, T. Su, and P. Bientinesi, "Hptt: a high-performance tensor transposition c++ library," in Proceedings of the 4th ACM SIGPLAN International Workshop on Libraries, Languages, and Compilers for Array Programming, 2017, pp. 56--62.
[36]
A.-P. Hynninen and D. I. Lyakh, "cutt: A high-performance tensor transpose library for cuda compatible gpus," arXiv preprint arXiv:1705.01598, 2017.
[37]
H. Cao and J. Chen, "Design and implementation of shenwei universal c/c++," arXiv e-prints, pp. arXiv-2208, 2022.
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Published In
November 2022
1277 pages
ISBN:9784665454445
- Conference Chairs:
- Felix Wolf,
- Sameer Shende,
- General Chair:
- Candace Culhane,
- Program Chairs:
- Sadaf Alam,
- Heike Jagode
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- IEEE CS
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IEEE Press
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Published: 18 November 2022
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SC '22: The International Conference for High Performance Computing, Networking, Storage and Analysis
November 13 - 18, 2022
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