A GPU-based quantum annealing simulator for fully-connected ising models utilizing spatial and temporal parallelism

Research output: Contribution to journalArticle

Abstract

Simulated quantum annealing (SQA) is a probabilistic approximation method to find a solution for a combinatorial optimization problem using digital computers. The processing time of SQA increases exponentially with the number of variables. Therefore, acceleration of SQA is regarded as a very important topic. However, 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 implement SQA in parallel on a GPU while preserving the data dependency. According to the experimental results, we have achieved over 97 times speed-up while maintaining the same accuracy-level compared to a single-core CPU implementation.

Original languageEnglish
Article number9057502
Pages (from-to)67929-67939
Number of pages11
JournalIEEE Access
Volume8
DOIs
Publication statusPublished - 2020

Keywords

  • GPU acceleration
  • Simulated quantum annealing
  • high performance computing
  • optimization problems

ASJC Scopus subject areas

  • Computer Science(all)
  • Materials Science(all)
  • Engineering(all)

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