A proof-of-principle experiment on the spontaneous symmetry breaking machine and numerical estimation of its performance on the $K_{2000}$ benchmark problem

TL;DR

This paper experimentally verifies a novel physical simulator, the SSBM, for combinatorial optimization, demonstrating its ability to explore stable states and its potential for large-scale problem solving.

Contribution

It provides the first experimental validation of the SSBM and numerical estimates of its performance on large benchmark problems.

Findings

SSBM can explore a single extremely stable state

Experimental results support SSBM's usefulness for large-scale problems

The phenomenon used in SSBM offers advantages over other simulators

Abstract

In a previous paper, we proposed a unique physically implemented type simulator for combinatorial optimization problems, called the spontaneous symmetry breaking machine (SSBM). In this paper, we first report the results of experimental verification of SSBM using a small-scale benchmark system, and then describe numerical simulations using the benchmark problems (K2000) conducted to confirm its usefulness for large-scale problems. From 1000 samples with different initial fluctuations, it became clear that SSBM can explore a single extremely stable state. This is based on the principle of a phenomenon used in SSBM, and could be a notable advantage over other simulators.