Quick Navigation

Topics

Trapped Ion Quantum Computing Quantum Simulation

A proof-of-principle experiment on the spontaneous symmetry breaking machine and numerical estimation of its performance on the K2000 benchmark problem

arXiv
Authors: Toshiya Sato, Takashi Goh

Year

2025

Paper ID

16039

Status

Preprint

Abstract Read

~2 min

Abstract Words

98

Citations

0

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.

Why This Paper Matters

  • This paper contributes to the Quantum Simulation research area in the Quantum Articles archive.
  • It adds a 2025 reference point for readers tracking recent quantum research.
  • In a previous paper, we proposed a unique physically implemented type simulator for combinatorial optimization problems, called the spontaneous symmetry breaking machine (SSBM).

Paper Tools

Become a member to use research tools

Sign in to open papers, visit source links, share, cite, compare, copy DOI links, request category corrections, and build your reading list.

Show Paper arXiv Publisher Share Cite This Paper Copy URL Compare Copy DOI Add to Reading List Category Correction Request

References & Citation Signals

Local Citation Graph (Related-Paper Links)

Current Paper #16039 #68474 Concentration-Free Quantum Kern... #68457 Quantum reservoir networks base... #68452 Sample-efficient benchmarking o... #68434 Lowering LCU Circuit Width thro...

External citation index: OpenAlex citation signal • updated 2026-06-12 12:50:15

Community Reactions

Quick sentiment from readers on this paper.

Score: 0
Likes: 0 Dislikes: 0

Sign in to react to this paper.

Discussion & Reviews (Moderated)

Average Rating: 0.0 / 5 (0 ratings)

No written reviews yet.