[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-128237-en":3,"doc-seo-128237-105":31,"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":28,"seo_description":14,"update_tm":29,"read_time":30},128237,2336475104736,"Quinn","https://ap-avatar.wpscdn.com/avatar/22000c4c5e0e5b17e70?x-image-process=image/resize,m_fixed,w_180,h_180&k=1786591360781797222",8,"Research & Report","AccelerQ - Accelerating Quantum Eigensolvers With Machine Learning on Quantum Simulators","AccelerQ presents a framework to automatically tune quantum eigensolver (QE) implementations by learning and optimizing hyperparameters using machine learning combined with search-based optimization and genetic algorithms. Instead of redesigning quantum algorithms or manually adjusting existing code, the approach treats QE implementations as black-box programs. It trains on data from smaller classically simulable systems and uses program-specific ML models to generalize to larger quantum systems, improving accuracy and efficiency when direct classical simulation is infeasible.","King’s Research Portal  \nDOI:  \n10.1145/3763132  \nDocument Version  \nPublisher's PDF, also known as Version of record  \nLink to publication record in King's Research Portal  \nCitation for published version (APA):  \nBensoussan, A. , Chachkarova, E. , Even-Mendoza, K. , Fortz, S. , & Lenihan, C. (2025) . AccelerQ: Accelerating Quantum Eigensolvers With Machine Learning on Quantum Simulators. Proceedings of the ACM on Programming Languages, 9(OOPSLA2), 2279-2309 . [https://doi.org/10.1145/3763132](https://doi.org/10.1145/3763132)  \nCiting this paper  \nPlease note that where the full-text provided on King's Research Portal is the Author Accepted Manuscript or Post-Print version this may differ from the final Published version. If citing, it is advised that you check and use the publisher's definitive version for pagination, volume/issue, and date of publication details. 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Dec. 2025  \nAccelerQ: Accelerating Quantum Eigensolvers with Machine Learning on Quantum Simulators  \nAVNER BENSOUSSAN, King’s College London, United Kingdom ELENA CHACHKAROVA, King’s College London, United Kingdom KARINE EVEN-MENDOZA, King’s College London, United Kingdom SOPHIE FORTZ, King’s College London, United Kingdom CONNOR LENIHAN, King’s College London, United Kingdom  \nWe present AccelerQ, a framework for automatically tuning quantum eigensolver (QE) implementations– these are quantum programs implementing a specific QE algorithm–using machine learning and searchbased optimisation. Rather than redesigning quantum algorithms or manually tweaking the code of an already existing implementation, AccelerQ treats QE implementations as black-box programs and learns tooptimise their hyperparameters to improve accuracy and efficiency by incorporating search-based techniques and genetic algorithms (GA) alongside ML models to efficiently explore the hyperparameter space of QE implementations and avoid local minima.  \nOur approach leverages two ideas: 1) train on data from smaller, classically simulable systems, and 2) use program-specific ML models, exploiting the fact that local physical interactions in molecular systems persist across scales, supporting generalisation to larger systems. We present an empirical evaluation of AccelerQ on two fundamentally different QE implementations: ADAPT-QSCI and QCELS. For each, we trained a QE predictor model, a lightweight XGBoost Python regressor, using data extracted classically from systems of up to 16 qubits. We deployed the model to optimise hyperparameters for executions on larger systems of 20-, 24-, and 28-qubit Hamiltonians, where direct classical simulation becomes impractical. We observed a reduction in error from 5.48% to 5.3% with only the ML model and further to 5.05% with GA for ADAPT-QSCI, and from 7.5% to 6.5%, with no additional gain with GA for QCELS. Given inconclusive results for some 20-and 24-qubit systems, we recommend further analysis of training data concerning Hamiltonia","cbCaiqc0ZjeoL8qd","https://ap.wps.com/l/cbCaiqc0ZjeoL8qd","pdf",1513348,2,1,32,"English","en",105,"# Introduction\n## AccelerQ framework\n## Training strategy and generalization\n## Empirical evaluation\n## Results and recommendations","[{\"question\":\"What problem does AccelerQ address in quantum eigensolvers?\",\"answer\":\"AccelerQ targets the challenge of efficiently tuning quantum eigensolver implementations’ hyperparameters to improve accuracy and efficiency, especially when classical simulation is impractical.\"},{\"question\":\"How does AccelerQ avoid manual tuning or redesigning algorithms?\",\"answer\":\"It treats QE implementations as black-box programs and learns to optimize hyperparameters using machine learning alongside search-based optimization and genetic algorithms.\"},{\"question\":\"What training and deployment strategy does AccelerQ use?\",\"answer\":\"AccelerQ trains models on data extracted from smaller, classically simulable systems and deploys the learned predictor to choose hyperparameters for larger systems running on quantum simulators.\"}]","AccelerQ - Accelerating Quantum Eigensolvers With Machine Learning on Quantum Simulators | PDF",1785945998,81,{"code":4,"msg":32,"data":33},"ok",{"site_id":25,"language":24,"slug":34,"title":13,"keywords":35,"description":14,"schema_data":36,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":29},"accelerq-accelerating-quantum-eigensolvers-with-machine-learning-on-quantum-simulators","",{"@graph":37,"@context":86},[38,54,69],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,48,51],{"item":42,"name":43,"@type":44,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":46,"name":47,"@type":44,"position":20},"https://docshare.wps.com/document/","Document",{"item":49,"name":12,"@type":44,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":44,"position":53},"https://docshare.wps.com/document/accelerq-accelerating-quantum-eigensolvers-with-machine-learning-on-quantum-simulators/128237/",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":42,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-23","2026-08-05",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},"What problem does AccelerQ address in quantum eigensolvers?","Question",{"text":76,"@type":77},"AccelerQ targets the challenge of efficiently tuning quantum eigensolver implementations’ hyperparameters to improve accuracy and efficiency, especially when classical simulation is impractical.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How does AccelerQ avoid manual tuning or redesigning algorithms?",{"text":81,"@type":77},"It treats QE implementations as black-box programs and learns to optimize hyperparameters using machine learning alongside search-based optimization and genetic algorithms.",{"name":83,"@type":74,"acceptedAnswer":84},"What training and deployment strategy does AccelerQ use?",{"text":85,"@type":77},"AccelerQ trains models on data extracted from smaller, classically simulable systems and deploys the learned predictor to choose hyperparameters for larger systems running on quantum simulators.","https://schema.org",{"og:url":52,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":52},"index,follow",{"doc_id":7,"site_id":25},{"code":4,"msg":5,"data":93},[94,98,102,106,111,116,121,124,129,132,136],{"id":21,"doc_module":4,"doc_module_name":47,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":20,"doc_module":4,"doc_module_name":47,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":47,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":47,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":112,"doc_module":4,"doc_module_name":47,"category_name":113,"show_sort_weight":114,"slug":115},6,"Technology",50,"technology",{"id":117,"doc_module":4,"doc_module_name":47,"category_name":118,"show_sort_weight":119,"slug":120},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":47,"category_name":12,"show_sort_weight":122,"slug":123},30,"research-report",{"id":125,"doc_module":4,"doc_module_name":47,"category_name":126,"show_sort_weight":127,"slug":128},9,"Religion & Spirituality",20,"religion-spirituality",{"id":127,"doc_module":4,"doc_module_name":47,"category_name":130,"show_sort_weight":127,"slug":131},"World Cup","world-cup",{"id":133,"doc_module":4,"doc_module_name":47,"category_name":134,"show_sort_weight":133,"slug":135},10,"Lifestyle","lifestyle",{"id":137,"doc_module":4,"doc_module_name":47,"category_name":138,"show_sort_weight":107,"slug":139},19,"General","general"]