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Quantum Machine Learning Quantum Simulation

Benchmarking 50-Photon Gaussian Boson Sampling on the Sunway TaihuLight

arXiv
Authors: Yuxuan Li, Mingcheng Chen, Yaojian Chen, Haitian Lu, Lin Gan, Chaoyang Lu, Jianwei Pan, Haohuan Fu, Guangwen Yang

Year

2020

Paper ID

21010

Status

Preprint

Abstract Read

~2 min

Abstract Words

139

Citations

N/A

Abstract

Boson sampling is expected to be one of an important milestones that will demonstrate quantum supremacy. The present work establishes the benchmarking of Gaussian boson sampling (GBS) with threshold detection based on the Sunway TaihuLight supercomputer. To achieve the best performance and provide a competitive scenario for future quantum computing studies, the selected simulation algorithm is fully optimized based on a set of innovative approaches, including a parallel scheme and instruction-level optimizing method. Furthermore, data precision and instruction scheduling are handled in a sophisticated manner by an adaptive precision optimization scheme and a DAG-based heuristic search algorithm, respectively. Based on these methods, a highly efficient and parallel quantum sampling algorithm is designed. The largest run enables us to obtain one Torontonian function of a 100 x 100 submatrix from 50-photon GBS within 20 hours in 128-bit precision and 2 days in 256-bit precision.

Why This Paper Matters

  • This paper contributes to the Quantum Machine Learning research area in the Quantum Articles archive.
  • It adds a 2020 reference point for readers tracking recent quantum research.
  • Boson sampling is expected to be one of an important milestones that will demonstrate quantum supremacy.

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