TY - GEN
T1 - Full-state quantum circuit simulation by using data compression
AU - Wu, Xin Chuan
AU - Di, Sheng
AU - Dasgupta, Emma Maitreyee
AU - Cappello, Franck
AU - Finkel, Hal
AU - Alexeev, Yuri
AU - Chong, Frederic T.
N1 - Funding Information:
This research was supported by the Exascale Computing Project (ECP), Project Number: 17-SC-20-SC, a collaborative effort of two DOE organizations - the Office of Science and the National Nuclear Security Administration, responsible for the planning and preparation of a capable exascale ecosystem, including software, applications, hardware, advanced system engineering and early testbed platforms, to support the nation’s exascale computing imperative. This research used the resources of the Argonne Leadership Computing Facility, which is a U.S. Department of Energy (DOE) Office of Science User Facility supported under Contract DE-AC02-06CH11357. Yuri Alexeev, Hal Finkel, and Xin-Chuan Ryan Wu were supported by the DOE Office of Science. This work is also supported by the National Science Foundation under Grant No. 1619253. This work is funded in part by EPiQC, an NSF Expedition in Computing, under grants CCF-1730449/1832377, and in part by STAQ, under grant NSF Phy-1818914.
Publisher Copyright:
© 2019 ACM.
PY - 2019/11/17
Y1 - 2019/11/17
N2 - Quantum circuit simulations are critical for evaluating quantum algorithms and machines. However, the number of state amplitudes required for full simulation increases exponentially with the number of qubits. In this study, we leverage data compression to reduce memory requirements, trading computation time and fidelity for memory space. Specifically, we develop a hybrid solution by combining the lossless compression and our tailored lossy compression method with adaptive error bounds at each timestep of the simulation. Our approach optimizes for compression speed and makes sure that errors due to lossy compression are uncorrelated, an important property for comparing simulation output with physical machines. Experiments show that our approach reduces the memory requirement of simulating the 61-qubit Grover's search algorithm from 32 exabytes to 768 terabytes of memory on Argonne's Theta supercomputer using 4,096 nodes. The results suggest that our techniques can increase the simulation size by 2∼16 qubits for general quantum circuits.
AB - Quantum circuit simulations are critical for evaluating quantum algorithms and machines. However, the number of state amplitudes required for full simulation increases exponentially with the number of qubits. In this study, we leverage data compression to reduce memory requirements, trading computation time and fidelity for memory space. Specifically, we develop a hybrid solution by combining the lossless compression and our tailored lossy compression method with adaptive error bounds at each timestep of the simulation. Our approach optimizes for compression speed and makes sure that errors due to lossy compression are uncorrelated, an important property for comparing simulation output with physical machines. Experiments show that our approach reduces the memory requirement of simulating the 61-qubit Grover's search algorithm from 32 exabytes to 768 terabytes of memory on Argonne's Theta supercomputer using 4,096 nodes. The results suggest that our techniques can increase the simulation size by 2∼16 qubits for general quantum circuits.
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U2 - 10.1145/3295500.3356155
DO - 10.1145/3295500.3356155
M3 - Conference contribution
AN - SCOPUS:85076137067
T3 - International Conference for High Performance Computing, Networking, Storage and Analysis, SC
BT - Proceedings of SC 2019
PB - IEEE Computer Society
T2 - 2019 International Conference for High Performance Computing, Networking, Storage and Analysis, SC 2019
Y2 - 17 November 2019 through 22 November 2019
ER -