[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-83634-en":3,"doc-seo-83634-105":30,"detail-sidebar-cat-0-en-105":92},{"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},83634,1649267921044,"Ava Thompson","https://us-avatar.wpscdn.com/avatar/1800007509477c92dfb?_k=1782875107921204101",8,"Research & Report","Extending the Computational Reach of Quantum Annealing Using Reverse Annealing","Quantum annealing is a heuristic for combinatorial optimization, but on current hardware performance drops on larger, more complex instances due to noise and small energy gaps. Reverse annealing has been proposed as an upgrade strategy, though its systematic benefit over forward annealing or longer runtimes remains uncertain. A D-Wave Advantage study shows combining forward and reverse annealing improves solution quality and efficiency across multiple problem classes, with strongest advantages as complexity increases. Reverse annealing achieves larger gains than merely extending forward annealing time.","arXiv :2607 .02146v1 [ quant-ph] 2 Jul 2026  \nExtending the computational reach of Quantum Annealing using Reverse Annealing  \nLucas Joshua Menger 1*, Thomas Lippert 1,2† and  \nManpreet Singh Jattana 1†  \n1* Modular Supercomputing and Quantum Computing, Institute of  \nComputer Science, Goethe University Frankfurt, Kettenhofweg, Frankfurt, 60325, Hessia, Germany.  \n2 J¨ulich Supercomputing Centre, Forschungszentrum J¨ulich GmbH, Wilhelm-Johnen-Straße, J¨ulich, 52428, North Rhine-Westphalia,  \nGermany.  \n*Corresponding author(s). E-mail(s): [menger@em.uni-frankfurt.de](menger@em.uni-frankfurt.de) ;  \nContributing authors: [t.lippert@em.uni-frankfurt.de](t.lippert@em.uni-frankfurt.de) ;  \n[jattana@em.uni-frankfurt.de](jattana@em.uni-frankfurt.de) ;  \n†These authors contributed equally to this work.  \nAbstract  \nQuantum annealing is a promising heuristic for combinatorial optimization, buton current hardware its performance degrades for larger and more complex problems due to noise and small energy gaps. Reverse annealing has been proposed as a refinement strategy, yet it remains unclear when it provides systematic advantages over standard forward annealing or simply increasing annealing time. We find that combining forward and reverse annealing consistently improves solution quality and efficiency across multiple problem classes. The benefits of reverse annealing increase with problem complexity and are strongest in regimes where forward annealing is increasingly limited. Moreover, reverse annealing yields larger efficiency gains than simply extending forward annealing times. We establish these results through a systematic experimental study on a D-Wave Advantage system, benchmarking reverse annealing across Max-Cut, Number Partitioning, and sparse clustering problems while varying reverse distance, pause duration, and annealing time. We identify a narrow optimal regime for reverse annealing parameters linked to the location of freeze-out points and energy-level crossings in the annealing schedule. These findings demonstrate that reverse annealing is most valuable for large, high-complexity optimization problems and  \n1  \nis likely to gain importance as quantum annealing hardware scales toward more realistic applications.  \nKeywords: keyword1, Keyword2, Keyword3, Keyword4  \n1 Introduction  \nFinding optimal solutions to complex combinatorial problems efficiently remains a fundamental challenge in domains such as logistics, finance, and materials design. Yet, many such problems are NP-hard, making exact optimizations on classical systems intractable. While there are many approximate solvers to those problems, they can struggle to find near optimal solutions systematically, especially in cases where heuristics fail [1–5] . This computational limitation has driven interest in alternative paradigms, including quantum optimization. Among these, quantum annealing has emerged as a promising metaheuristic, based on the adiabatic theorem, where quantum fluctuations help the system find a global minimum of an unconstrained combinatorial optimization problem more effectively than classical counterparts [6–13] . Unlike gate-based quantum systems, current quantum annealers offer larger qubit counts and lower error rates, positioning them as the most mature quantum hardware for practical optimization tasks [6, 14] . However, fundamental questions remain regarding how to best utilize and control these devices for real-world performance gains.  \nForward annealing (FA), the standard form of quantum annealing, has proven effective in solving quadratic unconstrained binary optimization (QUBO) problems. Another variant of quantum annealing is Reverse Annealing, a technique used as a refinement method that aims to improve on existing solution candidates. While both rely on the same underlying quantum dynamics, they differ in their initialization and evolution. Forward annealing begins from a superposition of all qubits and gradually increases the infl","cbCailmFESgIUpM9","https://ap.wps.com/l/cbCailmFESgIUpM9","pdf",3793450,5,1,28,"English","en",105,"# Abstract\n# Introduction\n## Motivation and background\n## Quantum annealing and forward annealing\n## Reverse annealing formulation","[{\"question\":\"Why does quantum annealing performance degrade on larger optimization problems?\",\"answer\":\"Performance degrades due to noise and small energy gaps that make it harder to maintain high-quality solutions as problem size and complexity increase.\"},{\"question\":\"How does reverse annealing differ from forward annealing?\",\"answer\":\"Forward annealing starts from a superposition ground state and gradually increases the problem Hamiltonian’s influence, while reverse annealing starts from a known classical candidate and reintroduces quantum effects to refine it.\"},{\"question\":\"What methodology and benchmark problems are used to evaluate reverse annealing?\",\"answer\":\"A systematic experimental study on a D-Wave Advantage system benchmarks reverse annealing on Max-Cut, Number Partitioning, and sparse clustering, varying reverse distance, pause duration, and annealing time.\"}]",1784189411,71,{"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":87,"head_meta":89,"extra_data":91,"updated_unix":28},"extending-the-computational-reach-of-quantum-annealing-using-reverse-annealing","",{"@graph":36,"@context":86},[37,54,69],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"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":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/extending-the-computational-reach-of-quantum-annealing-using-reverse-annealing/83634/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":24,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-07-27","2026-07-16",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"Why does quantum annealing performance degrade on larger optimization problems?","Question",{"text":76,"@type":77},"Performance degrades due to noise and small energy gaps that make it harder to maintain high-quality solutions as problem size and complexity increase.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How does reverse annealing differ from forward annealing?",{"text":81,"@type":77},"Forward annealing starts from a superposition ground state and gradually increases the problem Hamiltonian’s influence, while reverse annealing starts from a known classical candidate and reintroduces quantum effects to refine it.",{"name":83,"@type":74,"acceptedAnswer":84},"What methodology and benchmark problems are used to evaluate reverse annealing?",{"text":85,"@type":77},"A systematic experimental study on a D-Wave Advantage system benchmarks reverse annealing on Max-Cut, Number Partitioning, and sparse clustering, varying reverse distance, pause duration, and annealing 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