[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-83223-en":3,"doc-seo-83223-105":30,"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":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":13,"seo_description":14,"update_tm":28,"read_time":29},83223,962075114765,"Quinn","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",8,"Research & Report","Resource Efficient Hybrid Quantum Neighborhood Selection for Large Scale Molecular Diversity Optimization","Large-scale combinatorial optimization remains computationally demanding for classical heuristics, especially when dense QUBO formulations create major memory footprints, high CPU use, and long wall-clock times. This work studies a resource-efficient Hybrid Quantum Neighborhood Selection (HQNS) framework that decomposes large dense QUBO instances into bounded-width quantum subproblems via stochastic frontier selection. The method is evaluated on the NP-hard Maximum Diversity Subset Selection Problem (MDSSP) for molecular diversity, emphasizing quality–cost trade-offs. On benchmarks up to N=1000 candidates, HQNS retains 99.9908% of mean diversity while reducing wall-clock time by 94.91%, peak CPU by 64.68%, and peak memory by 88.61% versus a strong 11-restart parallel simulated annealing baseline.","Preprint submitted to arXiv.  \nDigital Object Identifier  \nResource-Efficient Hybrid Quantum Neighborhood Selection for Large-Scale Molecular Diversity Optimization  \nNICOLAS MENDES DE ARAÚJO2,3 AND LESTER DE ABREU FARIA1,2,3  \n1Technological Institute of Aeronautics (ITA), Electronic Division, Mal Eduardo Gomes, 50, São Paulo – SP, Brazil (e-mail: [lester@ita.br](lester@ita.br)) 2ICTQ Foton—Institute of Quantum Sciences and Technologies Foton, Brazil  \n3LACQ Feynman—Academic League of Quantum Computing (Liga Acadêmica de Computação Quântica Feynman), Brazil Corresponding author: Lester de Abreu Faria (e-mail: lester@ita.br) .  \nABSTRACT Large-scale combinatorial optimization remains computationally demanding even for mature classical heuristics, particularly when dense Quadratic Unconstrained Binary Optimization (QUBO)  \nformulations induce large memory footprints, high CPU utilization, and long wall-clock execution times. While near-term quantum processors are not yet capable of delivering unconditional quantum advantage for such problems, hybrid quantum–classical architectures may provide practical value by reducing the computational resource burden associated with large-scale optimization workflows. This paper presents aresource-efficiency study of Hybrid Quantum Neighborhood Selection (HQNS), a hybrid quantum–classical framework that decomposes large dense QUBO instances into bounded-width quantum subproblems through stochastic frontier selection. The method is evaluated on the Maximum Diversity Subset Selection Problem (MDSSP), a representative NP-hard problem with relevance to molecular diversity selection. Rather than claiming strict dominance in final solution quality, the study focuses on the trade-off between solution quality retention and computational resource consumption. Experimental results on molecular diversity benchmarks up to N = 1000 candidates show that HQNS preserves 99.9908% of the mean diversity score achieved by an 11-restart parallel Simulated Annealing baseline while reducing wall-clock time by 94.91%, peak CPU utilization by 64.68%, and peak memory usage by 88.61% relative to that strongest quality-oriented baseline. The QPU execution time remains bounded within an approximately 6– 7 second envelope across increasing problem scales, indicating that the quantum execution component is effectively decoupled from the global QUBO dimension when the frontier size is fixed. These results suggest that HQNS provides a resource-aware pathway for deploying hybrid quantum optimization in practical large-scale settings, not as evidence of unconditional quantum advantage, but as a computationally efficient architecture for incorporating near-term quantum processors into classical optimization pipelines.  \nINDEX TERMS Hybrid quantum–classical optimization, resource-efficient optimization, quantum optimization, QUBO, NISQ computing, molecular diversity selection, maximum diversity subset selection, simulated annealing, quantum neighborhood selection, stochastic frontier decomposition, computational resource efficiency, variational quantum algorithms.  \nINTRODUCTION provide a unified mathematical representation for a broad  \nclass of NP-hard optimization problems, dense interaction  \nLarge-scale combinatorial optimization remains a central computational bottleneck in science, engineering, logistics, finance, and molecular discovery. Many practically relevant problems can be formulated as Quadratic Unconstrained Binary Optimization (QUBO) instances, where the objective function depends on pairwise interactions among binary decision variables [1]–[3] . Although QUBO formulations  \nmatrices impose substantial computational pressure on classical solvers, particularly in terms of memory footprint, CPU utilization, and wall-clock execution time.  \nThe Maximum Diversity Subset Selection Problem (MDSSP) is a representative example of this challenge. Given a library of N candidate elements and a pairwise  \nsimilarity or dis","cbCaie3zIctJCArq","https://ap.wps.com/l/cbCaie3zIctJCArq","pdf",1226253,3,1,15,"English","en",105,"# Abstract\n# Introduction\n## QUBO as a bottleneck in large-scale optimization\n## MDSSP in molecular discovery\n## Limits of monolithic QAOA on NISQ hardware\n## Hybrid quantum–classical optimization motivation","[{\"question\":\"What problem does the paper target?\",\"answer\":\"The paper targets large-scale combinatorial optimization expressed as dense QUBO instances, focusing on the Maximum Diversity Subset Selection Problem (MDSSP) arising in molecular diversity selection.\"},{\"question\":\"How does HQNS reduce computational resource requirements?\",\"answer\":\"HQNS decomposes large dense QUBO instances into bounded-width quantum subproblems using stochastic frontier selection, effectively decoupling QPU execution from the global QUBO dimension when frontier size is fixed.\"},{\"question\":\"What performance trade-offs does the study emphasize compared with simulated annealing?\",\"answer\":\"The study focuses on the trade-off between retaining solution quality and reducing computational resource consumption, reporting near-preservation of mean diversity (99.9908%) while substantially cutting wall-clock time, peak CPU utilization, and peak memory versus a strong 11-restart parallel simulated annealing baseline.\"}]",1784186040,38,{"code":4,"msg":31,"data":32},"ok",{"site_id":25,"language":24,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"resource-efficient-hybrid-quantum-neighborhood-selection-for-large-scale-molecular-diversity-optimization","",{"@graph":36,"@context":85},[37,53,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,50],{"item":41,"name":42,"@type":43,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":20},"https://docshare.wps.com/document/research-report/",{"item":51,"name":13,"@type":43,"position":52},"https://docshare.wps.com/document/resource-efficient-hybrid-quantum-neighborhood-selection-for-large-scale-molecular-diversity-optimization/83223/",4,{"url":51,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":24,"description":14,"dateModified":61,"datePublished":62,"encodingFormat":60,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":41,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-07-24","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 problem does the paper target?","Question",{"text":75,"@type":76},"The paper targets large-scale combinatorial optimization expressed as dense QUBO instances, focusing on the Maximum Diversity Subset Selection Problem (MDSSP) arising in molecular diversity selection.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does HQNS reduce computational resource requirements?",{"text":80,"@type":76},"HQNS decomposes large dense QUBO instances into bounded-width quantum subproblems using stochastic frontier selection, effectively decoupling QPU execution from the global QUBO dimension when frontier size is fixed.",{"name":82,"@type":73,"acceptedAnswer":83},"What performance trade-offs does the study emphasize compared with simulated annealing?",{"text":84,"@type":76},"The study focuses on the trade-off between retaining solution quality and reducing computational resource consumption, reporting near-preservation of mean diversity (99.9908%) while substantially cutting wall-clock time, 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