Shunsuke Tsukiyama, Koji Nakano, Yasuaki Ito, Takumi Kato, Yuya Kawamata
{"title":"Ising Models for Solving the N-Queens Puzzle Based on the Domain-Wall Vectors","authors":"Shunsuke Tsukiyama, Koji Nakano, Yasuaki Ito, Takumi Kato, Yuya Kawamata","doi":"10.1002/cpe.8364","DOIUrl":null,"url":null,"abstract":"<div>\n \n <p>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 <span></span><math>\n <semantics>\n <mrow>\n <mo>−</mo>\n <mn>1</mn>\n </mrow>\n <annotation>$$ -1 $$</annotation>\n </semantics></math> or <span></span><math>\n <semantics>\n <mrow>\n <mo>+</mo>\n <mn>1</mn>\n </mrow>\n <annotation>$$ +1 $$</annotation>\n </semantics></math>. 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 <span></span><math>\n <semantics>\n <mrow>\n <mi>n</mi>\n </mrow>\n <annotation>$$ n $$</annotation>\n </semantics></math>-Queens puzzle. The conventional Ising model for this puzzle involves <span></span><math>\n <semantics>\n <mrow>\n <mfrac>\n <mrow>\n <mn>5</mn>\n </mrow>\n <mrow>\n <mn>3</mn>\n </mrow>\n </mfrac>\n <msup>\n <mrow>\n <mi>n</mi>\n </mrow>\n <mrow>\n <mn>3</mn>\n </mrow>\n </msup>\n <mo>−</mo>\n <mn>2</mn>\n <msup>\n <mrow>\n <mi>n</mi>\n </mrow>\n <mrow>\n <mn>2</mn>\n </mrow>\n </msup>\n <mo>+</mo>\n <mfrac>\n <mrow>\n <mi>n</mi>\n </mrow>\n <mrow>\n <mn>3</mn>\n </mrow>\n </mfrac>\n </mrow>\n <annotation>$$ \\frac{5}{3}{n}^3-2{n}^2+\\frac{n}{3} $$</annotation>\n </semantics></math> quadratic terms, with the maximum absolute value of coefficients being <span></span><math>\n <semantics>\n <mrow>\n <mn>4</mn>\n <mi>n</mi>\n <mo>+</mo>\n <mo>(</mo>\n <mi>n</mi>\n <mspace></mspace>\n <mo>mod</mo>\n <mspace></mspace>\n <mn>2</mn>\n <mo>)</mo>\n <mo>−</mo>\n <mn>7</mn>\n </mrow>\n <annotation>$$ 4n+\\left(n\\kern0.2em \\operatorname{mod}\\kern0.2em 2\\right)-7 $$</annotation>\n </semantics></math>. Our novel Ising model significantly reduces the number of quadratic terms to only <span></span><math>\n <semantics>\n <mrow>\n <mn>12</mn>\n <msup>\n <mrow>\n <mi>n</mi>\n </mrow>\n <mrow>\n <mn>2</mn>\n </mrow>\n </msup>\n <mo>−</mo>\n <mn>24</mn>\n <mi>n</mi>\n <mo>+</mo>\n <mn>12</mn>\n </mrow>\n <annotation>$$ 12{n}^2-24n+12 $$</annotation>\n </semantics></math>, with a maximum absolute coefficient of 6. Furthermore, we provide embedding results for a quantum annealer D-Wave Advantage utilizing a Pegasus graph <span></span><math>\n <semantics>\n <mrow>\n <mi>P</mi>\n <mo>(</mo>\n <mn>16</mn>\n <mo>)</mo>\n </mrow>\n <annotation>$$ P(16) $$</annotation>\n </semantics></math>. 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.</p>\n </div>","PeriodicalId":55214,"journal":{"name":"Concurrency and Computation-Practice & Experience","volume":"37 3","pages":""},"PeriodicalIF":1.5000,"publicationDate":"2025-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Concurrency and Computation-Practice & Experience","FirstCategoryId":"94","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1002/cpe.8364","RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"COMPUTER SCIENCE, SOFTWARE ENGINEERING","Score":null,"Total":0}
引用次数: 0
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.
期刊介绍:
Concurrency and Computation: Practice and Experience (CCPE) publishes high-quality, original research papers, and authoritative research review papers, in the overlapping fields of:
Parallel and distributed computing;
High-performance computing;
Computational and data science;
Artificial intelligence and machine learning;
Big data applications, algorithms, and systems;
Network science;
Ontologies and semantics;
Security and privacy;
Cloud/edge/fog computing;
Green computing; and
Quantum computing.