Experiments on Quantum Annealing

Kenichi Kurihara, Shu Tanaka, Seiji Miyashita

This paper studies quantum annealing (QA) for clustering, which can be seen as an extension of simulated annealing (SA). We derive a QA algorithm for clustering and propose an annealing schedule, which is crucial in practice. Experiments show the proposed QA algorithm finds better clustering assignments than SA. Furthermore, QA is as easy as SA to implement.

Eliahu Cohen, Boaz Tamir

Sergio Boixo

Kenichi Kurihara, Shu Tanaka, Seiji Miyashita

This paper studies quantum annealing (QA) for clustering, which can be seen as an extension of simulated annealing (SA). We derive a QA algorithm for clustering and propose an annealing schedule, which is crucial in practice. Experiments show the proposed QA algorithm finds better clustering assignments than SA. Furthermore, QA is as easy as SA to implement.

Jiei IWAMA, Hironori Makino

Domain wall encoding (DWE) is a new computational technique that allows the quantum annealing (QA)to be applied to the continuous variable optimizations.This study verifies by the numerical experiments that the reverse annealing (RA), an auxiliary computational technique of QA, works effectively for QA incorporating DWE and improves the probability of deriving the optimal solution.

秀稔 西森, Hidetoshi Nishimori