{"title":"使用基于模拟退火的等效机和量子退火器的混合优化方法","authors":"Shuta Kikuchi, N. Togawa, Shu Tanaka","doi":"10.7566/JPSJ.92.124002","DOIUrl":null,"url":null,"abstract":"Ising machines have the potential to realize fast and highly accurate solvers for combinatorial optimization problems. They are classified based on their internal algorithms. Examples include simulated-annealing-based Ising machines (non-quantum-type Ising machines) and quantum-annealing-based Ising machines (quantum annealers). Herein we propose a hybrid optimization method, which utilizes the advantages of both types. In this hybrid optimization method, the preprocessing step is performed by solving the non-quantum-annealing Ising machine multiple times. Then sub-Ising models with a reduced size by spin fixing are solved using a quantum annealer. The performance of the hybrid optimization method is evaluated via simulations using Simulated Annealing (SA) as a non-quantum-type Ising machine and D-Wave Advantage as a quantum annealer. Additionally, we investigate the parameter dependence of the proposed hybrid optimization method. The hybrid optimization method outperforms the preprocessing SA and the quantum annealing machine alone in fully connected random Ising models.","PeriodicalId":509167,"journal":{"name":"Journal of the Physical Society of Japan","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2023-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Hybrid Optimization Method Using Simulated-Annealing-Based Ising Machine and Quantum Annealer\",\"authors\":\"Shuta Kikuchi, N. Togawa, Shu Tanaka\",\"doi\":\"10.7566/JPSJ.92.124002\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Ising machines have the potential to realize fast and highly accurate solvers for combinatorial optimization problems. They are classified based on their internal algorithms. Examples include simulated-annealing-based Ising machines (non-quantum-type Ising machines) and quantum-annealing-based Ising machines (quantum annealers). Herein we propose a hybrid optimization method, which utilizes the advantages of both types. In this hybrid optimization method, the preprocessing step is performed by solving the non-quantum-annealing Ising machine multiple times. Then sub-Ising models with a reduced size by spin fixing are solved using a quantum annealer. The performance of the hybrid optimization method is evaluated via simulations using Simulated Annealing (SA) as a non-quantum-type Ising machine and D-Wave Advantage as a quantum annealer. Additionally, we investigate the parameter dependence of the proposed hybrid optimization method. The hybrid optimization method outperforms the preprocessing SA and the quantum annealing machine alone in fully connected random Ising models.\",\"PeriodicalId\":509167,\"journal\":{\"name\":\"Journal of the Physical Society of Japan\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-06-11\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of the Physical Society of Japan\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.7566/JPSJ.92.124002\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of the Physical Society of Japan","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.7566/JPSJ.92.124002","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
摘要
伊辛机具有为组合优化问题实现快速、高精度求解的潜力。它们根据内部算法进行分类。例如,基于模拟退火的伊辛机(非量子型伊辛机)和基于量子退火的伊辛机(量子退火器)。在这里,我们提出了一种混合优化方法,它利用了这两种方法的优点。在这种混合优化方法中,预处理步骤是多次求解非量子退火伊辛机。然后使用量子退火器求解通过自旋固定缩小尺寸的子伊辛模型。通过使用模拟退火(SA)作为非量子型伊辛机和 D-Wave Advantage 作为量子退火器进行模拟,评估了混合优化方法的性能。此外,我们还研究了所提出的混合优化方法的参数依赖性。在全连接随机伊辛模型中,混合优化方法优于预处理 SA 和单独的量子退火机。
Hybrid Optimization Method Using Simulated-Annealing-Based Ising Machine and Quantum Annealer
Ising machines have the potential to realize fast and highly accurate solvers for combinatorial optimization problems. They are classified based on their internal algorithms. Examples include simulated-annealing-based Ising machines (non-quantum-type Ising machines) and quantum-annealing-based Ising machines (quantum annealers). Herein we propose a hybrid optimization method, which utilizes the advantages of both types. In this hybrid optimization method, the preprocessing step is performed by solving the non-quantum-annealing Ising machine multiple times. Then sub-Ising models with a reduced size by spin fixing are solved using a quantum annealer. The performance of the hybrid optimization method is evaluated via simulations using Simulated Annealing (SA) as a non-quantum-type Ising machine and D-Wave Advantage as a quantum annealer. Additionally, we investigate the parameter dependence of the proposed hybrid optimization method. The hybrid optimization method outperforms the preprocessing SA and the quantum annealing machine alone in fully connected random Ising models.