IEEE International Conference on Quantum Computing and Engineering (QCE)

Error Reduction via Inter-Replica Interactions for Ising Machines

Tetsuro Abe, Kanta Hino, Shu Tanaka

Published Aug 30, 2025• 1 citation

Abstract

Ising machines, quantum annealing (QA) machines, are promising for solving combinatorial optimization problems at high speed. Problems are formulated as Ising models where optimal solutions correspond to ground states, but stochastic noise often causes deviations, necessitating error reduction strategies. The quantum annealing correction (QAC) model introduces penalty spins to favor consistent solutions across replicas and has shown effectiveness in QA, but its behavior under simulated annealing (SA) remains insufficiently understood. This study systematically evaluates the stacked model, a recently proposed error reduction method, against QAC-based and simple parallelization approaches in SA. Our results demonstrate that, even for large-scale systems, the stacked model consistently outperforms alternatives as the number of replicas increases, maintaining relatively high success probability through spontaneous replica alignment via structured coupling. These findings highlight the stacked model as a scalable and error reduction framework for Ising-based optimization.

DOI: 10.1109/qce65121.2025.10431

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