Boyuan Zhang
Ang Li
Dingwen Tao
ABSTRACT
Quantum circuit simulations are applied in more and more circumstances as the quantum computing community becomes broader. It helps researchers to evaluate the quantum algorithms and relieve the burden of limited quantum computing resources. However, most of the state-of-the-art quantum simulators utilizes either CPU or GPU to store and calculate the state vector, which results in resources stravation. Morever, the mamximum number of qubits supported by simulator is bounded by the memory, since the memory utilization increases exponentially with the number of qubits. In this study, we leverage Heterogeneous computing to utilize both CPU and GPU to store and update state vectors. We also integrate lossy data compression to reduce memory requirements. Specifically, we develop a heterogeous framework that has a dynamic scheduler to fully utilize the computing resources. We apply lossy compression to chunked state vector to make the maximum number of qubits higher than the regular simulators, the compression also benifits the data movement between CPU and GPU.
- M Ainsworth, O Tugluk, B Whitney, and S Klasky. Mgard: A multilevel technique for compression of floating-point data. In DRBSD-2 Workshop at Supercomputing, 2017.Google Scholar
- Frank Arute, Kunal Arya, Ryan Babbush, Dave Bacon, Joseph Bardin, Rami Barends, Rupak Biswas, Sergio Boixo, Fernando Brandao, David Buell, Brian Burkett, Yu Chen, Jimmy Chen, Ben Chiaro, Roberto Collins, William Courtney, Andrew Dunsworth, Edward Farhi, Brooks Foxen, Austin Fowler, Craig Michael Gidney, Marissa Giustina, Rob Graff, Keith Guerin, Steve Habegger, Matthew Harrigan, Michael Hartmann, Alan Ho, Markus Rudolf Hoffmann, Trent Huang, Travis Humble, Sergei Isakov, Evan Jeffrey, Zhang Jiang, Dvir Kafri, Kostyantyn Kechedzhi, Julian Kelly, Paul Klimov, Sergey Knysh, Alexander Korotkov, Fedor Kostritsa, Dave Landhuis, Mike Lindmark, Erik Lucero, Dmitry Lyakh, Salvatore Mandrà, Jarrod Ryan McClean, Matthew McEwen, Anthony Megrant, Xiao Mi, Kristel Michielsen, Masoud Mohseni, Josh Mutus, Ofer Naaman, Matthew Neeley, Charles Neill, Murphy Yuezhen Niu, Eric Ostby, Andre Petukhov, John Platt, Chris Quintana, Eleanor G. Rieffel, Pedram Roushan, Nicholas Rubin, Daniel Sank, Kevin J. Satzinger, Vadim Smelyanskiy, Kevin Jeffery Sung, Matt Trevithick, Amit Vainsencher, Benjamin Villalonga, Ted White, Z. Jamie Yao, Ping Yeh, Adam Zalcman, Hartmut Neven, and John Martinis. Quantum supremacy using a programmable superconducting processor. Nature, 574(7779):505--510, 2019.Google ScholarCross Ref
- Rafael Ballester-Ripoll, Peter Lindstrom, and Renato Pajarola. Tthresh: Tensor compression for multidimensional visual data. IEEE transactions on visualization and computer graphics, 26(9):2891--2903, 2019.Google Scholar
- Kerstin Beer, Dmytro Bondarenko, Terry Farrelly, Tobias J Osborne, Robert Salzmann, Daniel Scheiermann, and Ramona Wolf. Training deep quantum neural networks. Nature communications, 11(1):808, 2020.Google ScholarCross Ref
- Sergio Boixo, Sergei V Isakov, Vadim N Smelyanskiy, Ryan Babbush, Nan Ding, Zhang Jiang, Michael J Bremner, John M Martinis, and Hartmut Neven. Characterizing quantum supremacy in near-term devices. Nature Physics, 14(6):595--600, 2018.Google ScholarCross Ref
- cuZFP. https://github.com/LLNL/zfp/tree/develop/src/cuda_zfp, 2019. Online.Google Scholar
- Sheng Di and Franck Cappello. Fast error-bounded lossy HPC data compression with SZ. In 2016 IEEE International Parallel and Distributed Processing Symposium, pages 730--739, Chicago, IL, USA, 2016. IEEE.Google ScholarCross Ref
- Jun Doi, Hitomi Takahashi, Rudy Raymond, Takashi Imamichi, and Hiroshi Horii. Quantum computing simulator on a heterogenous hpc system. In Proceedings of the 16th ACM International Conference on Computing Frontiers, pages 85--93, 2019.Google ScholarDigital Library
- Edward Farhi, Jeffrey Goldstone, and Sam Gutmann. A quantum approximate optimization algorithm. arXiv preprint arXiv:1411.4028, 2014.Google Scholar
- Edward Farhi and Hartmut Neven. Classification with quantum neural networks on near term processors. arXiv preprint arXiv:1802.06002, 2018.Google Scholar
- Tianyu Feng, Siyan Chen, Xin You, Shuzhang Zhong, Hailong Yang, Zhongzhi Luan, and Depei Qian. dgquest: Accelerating large scale quantum circuit simulation through hybrid cpu-gpu memory hierarchies. In Network and Parallel Computing: 18th IFIP WG 10.3 International Conference, NPC 2021, Paris, France, November 3--5, 2021, Proceedings 18, pages 16--27. Springer, 2022.Google Scholar
- Gian Giacomo Guerreschi and Anne Y Matsuura. Qaoa for max-cut requires hundreds of qubits for quantum speed-up. Scientific reports, 9(1):1--7, 2019.Google ScholarCross Ref
- David A Huffman. A method for the construction of minimum-redundancy codes. Proceedings of the IRE, 40(9):1098--1101, 1952.Google ScholarCross Ref
- Tyson Jones, Anna Brown, Ian Bush, and Simon C Benjamin. Quest and high performance simulation of quantum computers. Scientific reports, 9(1):1--11, 2019.Google ScholarCross Ref
- Abhinav Kandala, Antonio Mezzacapo, Kristan Temme, Maika Takita, Markus Brink, Jerry M Chow, and Jay M Gambetta. Hardware-efficient variational quantum eigensolver for small molecules and quantum magnets. nature, 549(7671):242--246, 2017.Google Scholar
- Ang Li, Bo Fang, Christopher Granade, Guen Prawiroatmodjo, Bettina Heim, Martin Roetteler, and Sriram 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, pages 1--14, 2021.Google Scholar
- Xin Liang, Sheng Di, Dingwen Tao, Sihuan Li, Shaomeng Li, Hanqi Guo, Zizhong Chen, and Franck Cappello. Error-controlled lossy compression optimized for high compression ratios of scientific datasets. In 2018 IEEE International Conference on Big Data, pages 438--447. IEEE, 2018.Google ScholarCross Ref
- Peter Lindstrom. Fixed-rate compressed floating-point arrays. IEEE Transactions on Visualization and Computer Graphics, 20(12):2674--2683, 2014.Google ScholarCross Ref
- John Preskill. Quantum computing in the nisq era and beyond. Quantum, 2:79, 2018.Google ScholarCross Ref
- Quantum-centric supercomputing: The next wave of computing. https://research.ibm.com/blog/next-wave-quantum-centric-supercomputing, 2022. Online.Google Scholar
- Jonathan Romero, Ryan Babbush, Jarrod R McClean, Cornelius Hempel, Peter J Love, and Alán Aspuru-Guzik. Strategies for quantum computing molecular energies using the unitary coupled cluster ansatz. Quantum Science and Technology, 4(1):014008, 2018.Google ScholarCross Ref
- Dingwen Tao, Sheng Di, Zizhong Chen, and Franck Cappello. Significantly improving lossy compression for scientific data sets based on multidimensional prediction and error-controlled quantization. In 2017 IEEE International Parallel and Distributed Processing Symposium, pages 1129--1139, Orlando, FL, USA, 2017. IEEE.Google ScholarCross Ref
- Jiannan Tian, Sheng Di, Kai Zhao, Cody Rivera, Megan Hickman Fulp, Robert Underwood, Sian Jin, Xin Liang, Jon Calhoun, Dingwen Tao, et al. Cusz: An efficient gpu-based error-bounded lossy compression framework for scientific data. arXiv preprint arXiv:2007.09625, 2020.Google Scholar
- Terry A. Welch. A technique for high-performance data compression. Computer, 17(06):8--19, 1984.Google ScholarDigital Library
- Xin-Chuan Wu, Sheng Di, Emma Maitreyee Dasgupta, Franck Cappello, Hal Finkel, Yuri Alexeev, and Frederic 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, pages 1--24, 2019.Google ScholarDigital Library
- Boyuan Zhang, Jiannan Tian, Sheng Di, Xiaodong Yu, Yunhe Feng, Xin Liang, Dingwen Tao, and Franck Cappello. Fz-gpu: A fast and high-ratio lossy compressor for scientific computing applications on gpus. arXiv preprint arXiv:2304.12557, 2023.Google Scholar
- Boyuan Zhang, Jiannan Tian, Sheng Di, Xiaodong Yu, Martin Swany, Dingwen Tao, and Franck Cappello. Gpulz: Optimizing lzss lossless compression for multi-byte data on modern gpus. arXiv preprint arXiv:2304.07342, 2023.Google Scholar
- Chen Zhang, Haojie Wang, Zixuan Ma, Lei Xie, Zeyu Song, and Jidong Zhai. Uniq: a unified programming model for efficient quantum circuit simulation. In 2022 SC22: International Conference for High Performance Computing, Networking, Storage and Analysis (SC), pages 692--707. IEEE Computer Society, 2022.Google ScholarCross Ref
- Jacob Ziv and Abraham Lempel. A universal algorithm for sequential data compression. IEEE Transactions on information theory, 23(3):337--343, 1977.Google ScholarDigital Library
Index Terms
- HQ-Sim: High-performance State Vector Simulation of Quantum Circuits on Heterogeneous HPC Systems
Recommendations
SV-sim: scalable PGAS-based state vector simulation of quantum circuits
SC '21: Proceedings of the International Conference for High Performance Computing, Networking, Storage and AnalysisHigh-performance quantum circuit simulation in a classic HPC is still imperative in the NISQ era. Observing that the major obstacle of scalable state-vector quantum simulation arises from the massively fine-grained irregular data-exchange with remote ...
Quantum computing and quantum simulation with group-II atoms
Recent experimental progress in controlling neutral group-II atoms for optical clocks, and in the production of degenerate gases with group-II atoms has given rise to novel opportunities to address challenges in quantum computing and quantum simulation. ...
Duality quantum computer and the efficient quantum simulations
Duality quantum computing is a new mode of a quantum computer to simulate a moving quantum computer passing through a multi-slit. It exploits the particle wave duality property for computing. A quantum computer with n qubits and a qudit simulates a ...
Comments