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Temporal and spatial parallel processing of simulated quantum annealing on a multicore CPU

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Abstract

Simulated quantum annealing (SQA) is a probabilistic approximation method to find a solution for a combinatorial optimization problem using a digital computer. It is possible to simulate large-scale optimization problems on a CPU due to its high external memory capacity. However, the processing time increases exponentially with the number of variables, and parallel implementation is difficult due to the serial nature of the quantum Monte Carlo algorithm used in SQA. In this paper, we propose a method to accelerate SQA on a multicore CPU, based on temporal and spatial parallel processing with high data localization. According to the experimental results using 16-core CPU, we achieved from 8 to 16 times speedup compared to single-core CPU implementations. The proposed method can be used to solve combinatorial optimization problems that have more than 64,000 variables, which was not possible using previous GPU- and FPGA-based accelerators.

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Acknowledgements

This research is partly supported by MEXT KAKENHI, grant numbers 19K11998 and 20H04197.

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Authors and Affiliations

  1. Graduate School of Information Sciences, Tohoku University, 6-3-09, Aramaki-Aza-Aoba, Aoba, Sendai, Miyagi, 980-8579, Japan

    Hasitha Muthumala Waidyasooriya & Masanori Hariyama

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  1. Hasitha Muthumala Waidyasooriya
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  2. Masanori Hariyama
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Correspondence to Hasitha Muthumala Waidyasooriya.

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Waidyasooriya, H.M., Hariyama, M. Temporal and spatial parallel processing of simulated quantum annealing on a multicore CPU. J Supercomput 78, 8733–8750 (2022). https://doi.org/10.1007/s11227-021-04242-0

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