[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127018-en":3,"doc-seo-127018-105":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":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":27,"seo_description":14,"update_tm":28,"read_time":29},127018,2336474466412,"Ezra","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Leveraging Quantum Machine Learning Generalization to Significantly Speed-up Quantum Compilation - Abstract and Overview","Quantum compilation faces costly O(4n) matrix-matrix operations in existing numerical optimizers used to instantiate quantum circuits. Inspired by quantum machine learning (QML), QFactor-Sample replaces the heavy computations with simpler O(2n) circuit simulations on sampled orthogonal training states. The method tunes hyperparameters and validates performance across many circuits, reducing compilation time and improving scalability in the BQSKit compiler. Benchmarks report an average speedup factor of 69 for circuits with more than 8 qubits, while enabling tradeoffs between compilation speed and solution quality through partitioning dynamics.","Leveraging Quantum Machine Learning Generalization to Significantly Speed-up Quantum Compilation  \nAlon Kukliansky 1 , Lukasz Cincio2 , Ed Younis3 , and Costin Iancu3  \n1 Naval Postgraduate School, 1 University Circle, Monterey, California 93943, USA 2 Theoretical Division, Los Alamos National Laboratory, Los Alamos, NM 87545, USA  \n3 Applied Mathematics and Computational Research Division, Lawrence Berkeley National Laboratory, Berkeley, California 94720, USA  \narXiv :2405 . 12866v2 [ quant-ph] 19 Aug 2024  \nExisting numerical optimizers deployed in quantum compilers use expensive O(4n ) matrix-matrix operations. Inspired by recent advances in quantum machine learning (QML), QFactor-Sample replaces matrix-matrix operations with simpler O(2n ) circuit simulations on a set of sample inputs. The simpler the circuit, the lower the number of required input samples. We validate QFactor-Sample on a large set of circuits and discuss its hyperparameter tuning. When incorporated in the BQSKit quantum compiler and compared against a state-of-the-art domain-specific optimizer, we demonstrate improved scalability anda reduction in compile time, achieving an average speedup factor of 69 for circuits with more than 8 qubits. We also discuss how improved numerical optimization affects the dynamics of partitioning-based compilation schemes, which allow a tradeoff between compilation speed and solution quality.  \n1 Introduction  \nGiven a parameterized quantum circuit and a target unitary, a common operation in quantum program development is to solve an optimization problem to determine the parameters that implement the target unitary. Solving for parameters is commonly referred to as instantiation, and it is an operation that appears in hybrid algorithms [1 , 2],  \nAlon Kukliansky: [alon.kukliansky.is@nps.edu](alon.kukliansky.is@nps.edu)  \nLukasz Cincio: [lcincio@lanl.gov](lcincio@lanl.gov)  \nEd Younis: [edyounis@lbl.gov](edyounis@lbl.gov)  \nCostin Iancu: [cciancu@lbl.gov](cciancu@lbl.gov)  \ncircuit synthesis [3 , 4 , 5 , 6 , 7 , 8 , 9] or within quantum machine learning (QML) [10 , 11 , 12 , 13] algorithms.  \nIn all existing approaches, the objective function in instantiation requires computing process distances [9] between two unitaries, an operation with O(4n ) complexity. As far as we know, the state-of-the-art is illustrated by the QFactor [14] domain-specific optimizer, which uses a tensor network formulation together with analytic methods and an iterative local optimization algorithm to reduce the effective number of problem parameters. The improvements over general-purpose optimizers (GPOs) come, among other features, from working at the unitary rather than the parameter level. A given gate may have a very complicated representation in terms of parameters that need to be resolved by GPOs. In contrast, QFactor optimizes that gate on each update.  \nQFactor improves performance and scalability by reducing the number of parameters by a (large) constant. In this paper, we show how to further gain O(2n ) speedup in computational complexity for the algorithm’s inner loop, while maintaining the same quality of results (QoRs) . Our benchmarks show an average reduction of 17X in runtime and an impressive 69X average reduction for circuits with 9-12 qubits. We have seen a runtime reduction of up to 830X for individual instantiation runs.  \nThe basic idea is taken from recent advances [15 , 16 , 17 , 18] in QML theory. Modern QML methods involve variationally optimizing a parameterized quantum circuit on a training dataset and subsequently making predictions on unseen data (i.e., generalizing) . It has been shown that for a quantum circuit with T  \nparametrized gates, that has been trained on M samples the generalization error is bounded by O 􀀒 qT~~ ~~logM~~ ~~T 􀀓 . We use a reduction from the instantiation problem to a traditional QML flow, linking the generalization error to the instantiation error and taking advantage of this bound to limit the","cbCaioxT9I4nhzJO","https://ap.wps.com/l/cbCaioxT9I4nhzJO","pdf",1380216,1,16,"English","en",105,"# Introduction\n# Background\n## Numerical Optimization and Instantiation\n## QML Generalization for Instantiation\n# QFactor-Sample Algorithm\n# Evaluation Procedures and Results\n# Discussion","[{\"question\":\"What problem in quantum compilation motivates QFactor-Sample?\",\"answer\":\"Existing instantiation-based optimizers rely on expensive O(4n) matrix-matrix operations when computing distances between unitaries, making compilation costly for larger circuits.\"},{\"question\":\"How does QFactor-Sample reduce computational complexity?\",\"answer\":\"It replaces matrix-matrix operations with O(2n) circuit simulations using sampled orthogonal states from a QML-style training dataset, optimizing based only on the samples.\"},{\"question\":\"What performance improvements are reported in the evaluation?\",\"answer\":\"Integrated into BQSKit and compared with a state-of-the-art domain-specific optimizer, QFactor-Sample shows improved scalability and compile-time reductions, including an average speedup factor of 69 for circuits with more than 8 qubits.\"}]","Leveraging Quantum Machine Learning Generalization to Significantly Speed-up Quantum Compilation - Abstract and Overview | PDF",1785936356,40,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":28},"leveraging-quantum-machine-learning-generalization-to-significantly-speed-up-quantum-compilation-abstract-and-overview","",{"@graph":36,"@context":86},[37,54,69],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"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/leveraging-quantum-machine-learning-generalization-to-significantly-speed-up-quantum-compilation-abstract-and-overview/127018/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"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","2026-08-22","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 in quantum compilation motivates QFactor-Sample?","Question",{"text":76,"@type":77},"Existing instantiation-based optimizers rely on expensive O(4n) matrix-matrix operations when computing distances between unitaries, making compilation costly for larger circuits.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How does QFactor-Sample reduce computational complexity?",{"text":81,"@type":77},"It replaces matrix-matrix operations with O(2n) circuit simulations using sampled orthogonal states from a QML-style training dataset, optimizing based only on the samples.",{"name":83,"@type":74,"acceptedAnswer":84},"What performance improvements are reported in the evaluation?",{"text":85,"@type":77},"Integrated into BQSKit and compared with a state-of-the-art domain-specific optimizer, QFactor-Sample shows improved scalability and compile-time reductions, including an average speedup factor of 69 for circuits with more than 8 qubits.","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":24},{"code":4,"msg":5,"data":93},[94,98,102,106,111,116,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":46,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":112,"doc_module":4,"doc_module_name":46,"category_name":113,"show_sort_weight":114,"slug":115},6,"Technology",50,"technology",{"id":117,"doc_module":4,"doc_module_name":46,"category_name":118,"show_sort_weight":29,"slug":119},7,"Healthcare","healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":107,"slug":138},19,"General","general"]