Ising Models for Solving the N‐Queens Puzzle Based on the Domain‐Wall Vectors

ABSTRACT An Ising model is a mathematical model defined by an objective function comprising a quadratic formula of multiple spin variables, each taking values of either or . The task of determining a spin value assignment to these variables that minimizes the resulting value of an Ising model is a challenging optimization problem. Recently, quantum annealers, consisting of qubit cells interconnected according to principles of quantum mechanics, have emerged as a solution for tackling such problems. Ising models characterized by fewer quadratic terms are preferable as they reduce the resource requirements of quantum annealers. Additionally, it is advantageous for the absolute values of coefficients associated with linear and quadratic terms to be small to facilitate the discovery of good solutions, given the inherent limitations in the resolution of quantum annealers. The primary contribution of this article lies in presenting Ising models tailored for solving the ‐Queens puzzle. The conventional Ising model for this puzzle involves quadratic terms, with the maximum absolute value of coefficients being . Our novel Ising model significantly reduces the number of quadratic terms to only , with a maximum absolute coefficient of 6. Furthermore, we provide embedding results for a quantum annealer D‐Wave Advantage utilizing a Pegasus graph . We succeeded in embedding our novel Ising model for up to the 21‐Queens puzzle, while the conventional Ising model can be embedded only for up to the 14‐Queens puzzle.

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Journal
Concurrency and Computation Practice and Experience
Published
2025-01-16
DOI
https://doi.org/10.1002/cpe.8364
Primary Topic
Computability, Logic, AI Algorithms
Type
article
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article

Ising Models for Solving the N‐Queens Puzzle Based on the Domain‐Wall Vectors

Yuya Kawamata, Yasuaki Ito, Takumi Kato, Koji Nakano et al.
Concurrency and Computation Practice and Experience
Computability, Logic, AI Algorithms
article

Ising Models for Solving the N‐Queens Puzzle Based on the Domain‐Wall Vectors

Yuya Kawamata, Yasuaki Ito, Takumi Kato, Koji Nakano, Shunsuke Tsukiyama
article en

Abstract

ABSTRACT An Ising model is a mathematical model defined by an objective function comprising a quadratic formula of multiple spin variables, each taking values of either or . The task of determining a spin value assignment to these variables that minimizes the resulting value of an Ising model is a challenging optimization problem. Recently, quantum annealers, consisting of qubit cells interconnected according to principles of quantum mechanics, have emerged as a solution for tackling such problems. Ising models characterized by fewer quadratic terms are preferable as they reduce the resource requirements of quantum annealers. Additionally, it is advantageous for the absolute values of coefficients associated with linear and quadratic terms to be small to facilitate the discovery of good solutions, given the inherent limitations in the resolution of quantum annealers. The primary contribution of this article lies in presenting Ising models tailored for solving the ‐Queens puzzle. The conventional Ising model for this puzzle involves quadratic terms, with the maximum absolute value of coefficients being . Our novel Ising model significantly reduces the number of quadratic terms to only , with a maximum absolute coefficient of 6. Furthermore, we provide embedding results for a quantum annealer D‐Wave Advantage utilizing a Pegasus graph . We succeeded in embedding our novel Ising model for up to the 21‐Queens puzzle, while the conventional Ising model can be embedded only for up to the 14‐Queens puzzle.

Concurrency and Computation Practice and ExperienceVol. 37(3)
Hiroshima University (JP), NTT (Japan) (JP), Scope Group (United States) (US)
Openalex Percentile: Top 99%
Computability, Logic, AI Algorithms
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Ising Models for Solving the N‐Queens Puzzle Based on the Domain‐Wall Vectors — Yuya Kawamata, Yasuaki Ito, et al. · Concurrency and Computation Practice and Experience (2025) | TGRS Research Map | TGRS