Experimental Evaluations of Parallel Tempering on an Ising Machine

Yosuke Mukasa; Shu Tanaka; Nozomu Togawa

doi:10.2197/ipsjtsldm.14.27

Yosuke Mukasa, Shu Tanaka, Nozomu Togawa

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Keywords: Ising machines, parallel tempering, combinatorial optimization problem, Ising model, quadratic unconstrained binary optimization

JOURNAL FREE ACCESS

2021 Volume 14 Pages 27-29

DOI https://doi.org/10.2197/ipsjtsldm.14.27

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Abstract

Ising machines have recently attracted much attention because they are expected to solve combinatorial optimization problems efficiently. We focus on an Ising machine whose algorithm is based on parallel tempering (PT), and experimentally evaluate the performance of the Ising machine for MIN-CUT problems. Experimental results show that the Ising machine outperforms a famous graph partitioning solver in terms of the quality of solution and the time-to-target-solution.

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