[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-82676-en":3,"doc-seo-82676-105":29,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":13,"seo_description":14,"update_tm":27,"read_time":28},82676,3848291630094,"Emma Wilson","https://eur-avatar.wpscdn.com/davatar_085a072bc5b1113ac321206ff7593b45",8,"Research & Report","Adaptive Enhanced Quantum inspired Simulated Bifurcation Algorithm for Population State Perception","Adaptive enhanced quantum-inspired simulated bifurcation methods are constrained by dynamic scheduling that cannot effectively adjust to different problem instances. They also face persistent exploration–exploitation imbalance during evolutionary search, mainly caused by static preset parameters and globally uniform strategies that reduce effectiveness and homogenize results. The AE-QSB framework introduces a population-state driven closed loop using perception indicators across four population states, and integrates three complementary strategies from efficient extremum seeking to density-aware adaptive scheduling.","arXiv :2607 .02540v1 [ cs .NE] 23 Jun 2026  \nSCIENCE CHINA  \nInformation Sciences  \n. RESEARCH PAPER .  \nAdaptive Enhanced Quantum-inspired Simulated Bifurcation Algorithm for Population State Perception  \nDongmei LIU 1* , Jian LI 1†* , Xiubo CHEN 1 & Jin-Tao WANG 1  \n1State Key Laboratory of Networking and Switching Technology,  \nCyber Security Center, School of Cyberspace Security,  \nBeijing University of Posts and Telecommunications, Beijing 100876, China  \n\n| Abstract Existing quantum-inspired simulated bifurcation algorithms rely on dynamic scheduling methods but lack the ability to adapt effectively to different problem instances. Additionally, during the evolutionary stage, balancing exploration and exploitation remains challenging. The fundamental issue stems from the widespread use of static preset parameters and globally uniform strategies, which can diminish algorithm effectiveness and lead to result homogenization. This article proposes an Adaptive Enhanced Quantum-inspired Simulated Bifurcation (AE-QSB) framework driven by population states. By leveraging perception indicators of four distinct population states, the QSB algorithm establishes a closed-loop strategy encompassing perception, decision-making, and execution. Within this framework, we introduce three complementary algorithms spanning a spectrum from efficient extremum seeking (ME-BSB), through population-level uniform refinement (SE-DSB), to density-aware adaptive scheduling (SG-DSB) . On the medium-sized graph G22, both SE-DSB and SG-DSB achieve a mean gap below 0.05%, while ME-BSB attains the optimal trade-off between runtime and solution quality with a gap of 0.26% and the shortest single-run time. We compared AE-QSB variants with other algorithms across all benchmark graphs from G1 to G81 . The results demonstrate that AE-QSB achieved the lowest mean gap on 74.6% of the graphs and the highest average approximation rate on 84.5% of the graphs. Ablation experiments further revealed that subgroup exploration and rescue mechanisms play crucial roles in both multifactor and single-factor components. This study demonstrates that population statistical information during dynamic evolution provides a computable and effective foundation for adaptive control, enabling quantum-inspired optimization methods to transition from fixed scheduling to data-driven closed-loop control.\u003Cbr>Keywords Simulated bifurcation, Maxcut, Adaptive dynamics, Population diversity, Quantum-inspired optimization algorithm. |\n| --- |\n| Citation\u003Cbr>. Sci China Inf Sci, for review |\n\n1 Introduction  \nThe combinatorial optimization problem is prevalent in various fields, including communication network design, computational biology, financial engineering, drug discovery, chip layout, and scientific computing. It represents one of the core challenges in information science and engineering optimization [1–7] . From the perspective of computational complexity, problems such as Maxcut, minimum vertex cover, graph coloring, Boolean satisfiability, portfolio optimization, and beamforming can all be formulated as Ising models.  \ns∈m{n1}N H (s) = − 12 Xi,j Jijsisj −Xi hisi , (1)  \nwhere si ∈ {±1} denotes the spin variable, Jij are the entries of the Ising coupling matrix J = −W, and hi is the local external field (complete sign conventions in Sec. 2) . These problems commonly exhibit high dimensionality, strong coupling, and significant non-convexity. In the energy landscape, the number of local minima increases exponentially with problem size, presenting a combinatorial explosion challenge for exact algorithms. Meanwhile, heuristic methods are sensitive to complex energy landscapes and tend to prematurely converge to suboptimal basins [8, 9] . Therefore, developing new optimization methods that integrate efficiency, scalability, and global search capabilities remains a central focus of research in this field.  \nResearch on the combinatorial optimization problem is gradually shifting from tradit","cbCaib02q9GgV2s6","https://ap.wps.com/l/cbCaib02q9GgV2s6","pdf",1283203,1,24,"English","en",105,"# Introduction\n## Background: Ising models and combinatorial optimization\n## Quantum-inspired and simulated bifurcation methods\n## Coherent Ising Machine and benchmark evaluation","[{\"question\":\"What limitation do existing quantum-inspired simulated bifurcation algorithms face?\",\"answer\":\"Existing methods rely on dynamic scheduling but cannot adapt effectively across different problem instances. They also struggle with exploration–exploitation balance during evolution due to static preset parameters and globally uniform strategies.\"},{\"question\":\"How does the AE-QSB framework improve adaptivity?\",\"answer\":\"AE-QSB is driven by population states and uses a closed-loop process of perception, decision-making, and execution. It leverages perception indicators from four distinct population states to adapt scheduling behavior.\"},{\"question\":\"What performance results are reported for different AE-QSB variants?\",\"answer\":\"On medium-sized graph G22, SE-DSB and SG-DSB achieve a mean gap below 0.05%, while ME-BSB yields the best runtime–solution trade-off with a 0.26% gap and the shortest single-run time. Across G1–G81, AE-QSB attains the lowest mean gap on 74.6% of graphs and the highest average approximation rate on 84.5%.\"}]",1784182218,60,{"code":4,"msg":30,"data":31},"ok",{"site_id":24,"language":23,"slug":32,"title":13,"keywords":33,"description":14,"schema_data":34,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":27},"adaptive-enhanced-quantum-inspired-simulated-bifurcation-algorithm-for-population-state-perception","",{"@graph":35,"@context":85},[36,53,68],{"@type":37,"itemListElement":38},"BreadcrumbList",[39,43,47,50],{"item":40,"name":41,"@type":42,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":44,"name":45,"@type":42,"position":46},"https://docshare.wps.com/document/","Document",2,{"item":48,"name":12,"@type":42,"position":49},"https://docshare.wps.com/document/research-report/",3,{"item":51,"name":13,"@type":42,"position":52},"https://docshare.wps.com/document/adaptive-enhanced-quantum-inspired-simulated-bifurcation-algorithm-for-population-state-perception/82676/",4,{"url":51,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":23,"description":14,"dateModified":61,"datePublished":62,"encodingFormat":60,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":40,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-07-17","2026-07-16",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What limitation do existing quantum-inspired simulated bifurcation algorithms face?","Question",{"text":75,"@type":76},"Existing methods rely on dynamic scheduling but cannot adapt effectively across different problem instances. They also struggle with exploration–exploitation balance during evolution due to static preset parameters and globally uniform strategies.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the AE-QSB framework improve adaptivity?",{"text":80,"@type":76},"AE-QSB is driven by population states and uses a closed-loop process of perception, decision-making, and execution. It leverages perception indicators from four distinct population states to adapt scheduling behavior.",{"name":82,"@type":73,"acceptedAnswer":83},"What performance results are reported for different AE-QSB variants?",{"text":84,"@type":76},"On medium-sized graph G22, SE-DSB and SG-DSB achieve a mean gap below 0.05%, while ME-BSB yields the best runtime–solution trade-off with a 0.26% gap and the shortest single-run time. Across G1–G81, AE-QSB attains the lowest mean gap on 74.6% of graphs and the highest average approximation rate on 84.5%.","https://schema.org",{"og:url":51,"og:type":87,"og:title":13,"og:site_name":58,"og:description":14},"article",{"robots":89,"canonical":51},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,109,114,119,122,127,130,134],{"id":20,"doc_module":4,"doc_module_name":45,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":46,"doc_module":4,"doc_module_name":45,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":52,"doc_module":4,"doc_module_name":45,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":45,"category_name":107,"show_sort_weight":28,"slug":108},5,"Comic","comic",{"id":110,"doc_module":4,"doc_module_name":45,"category_name":111,"show_sort_weight":112,"slug":113},6,"Technology",50,"technology",{"id":115,"doc_module":4,"doc_module_name":45,"category_name":116,"show_sort_weight":117,"slug":118},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":45,"category_name":12,"show_sort_weight":120,"slug":121},30,"research-report",{"id":123,"doc_module":4,"doc_module_name":45,"category_name":124,"show_sort_weight":125,"slug":126},9,"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":45,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":45,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":45,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]