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This work unifies zero noise extrapolation, Clifford-data regression, and virtual distillation under a single data-driven framework called UNITED. A trapped-ion noise model benchmarks UNITED against state-of-the-art methods for observables from random circuits and QAOA for Max-Cut. Results show performance depends strongly on shot budgets, with higher-accuracy requiring more shots, and UNITED leading at the largest studied budgets.",{"@graph":69,"@context":127},[70,84,106],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/unifying-and-benchmarking-state-of-the-art-quantum-error-mitigation-techniques-research-report/148672/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/unifying-and-benchmarking-state-of-the-art-quantum-error-mitigation-techniques-research-report/148672.png","ImageObject",300,407,{"name":92,"@type":93},"WPS_1786070896","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-17","2026-08-26",true,{"@type":102,"interactionType":103,"userInteractionCount":105},"InteractionCounter",{"@type":104},"ViewAction",13,{"@type":107,"mainEntity":108},"FAQPage",[109,115,119,123],{"name":110,"@type":111,"acceptedAnswer":112},"What common feature do many state-of-the-art error mitigation methods share in this work?","Question",{"text":113,"@type":114},"They are data-driven, relying on classical data obtained from runs of different quantum circuits.","Answer",{"name":116,"@type":111,"acceptedAnswer":117},"How does the UNITED framework unify ZNE, CDR, and VD?",{"text":118,"@type":114},"It treats them as using classical data produced from different circuit conditions—such as noise scaling or varying numbers of state preparations—within one general data-driven mitigation framework.",{"name":120,"@type":111,"acceptedAnswer":121},"What benchmarking setup is used to evaluate UNITED?",{"text":122,"@type":114},"A realistic noise model from a trapped-ion quantum computer benchmarks UNITED and other methods on observables from random circuits and QAOA for Max-Cut.",{"name":124,"@type":111,"acceptedAnswer":125},"How do shot budgets affect the performance of different mitigation techniques?",{"text":126,"@type":114},"Performance depends strongly on shot budgets: more powerful methods require more shots for optimal results, and UNITED gives the most accurate mitigation at the largest studied budget.","https://schema.org",{"og:url":83,"og:type":129,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":131,"canonical":83},"index,follow",{"doc_id":133,"site_id":62},148672,1787782886,{"code":4,"msg":5,"data":136},{"doc_id":133,"user_id":137,"nickname":92,"user_avatar":138,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":139,"file_id":140,"file_url":141,"file_type":142,"file_size":143,"view_count":105,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":144,"language":145,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":146,"faqs":147,"seo_title":148,"seo_description":67,"update_tm":134,"read_time":149,"preview_page_count":4},549768072016,"https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d","Unifying and benchmarking state-of-the-art quantum error mitigation techniques  \nDaniel Bultrini 1,2 , Max Hunter Gordon3 , Piotr Czarnik 1,4 , Andrew Arrasmith 1,5 , M. Cerezo6,5 , Patrick J. Coles 1,5 , and Lukasz Cincio 1,5  \n1 Theoretical Division, Los Alamos National Laboratory, Los Alamos, NM 87545, USA  \n2 Theoretische Chemie, Physikalisch-Chemisches Institut, Universität Heidelberg, INF 229, D-69120 Heidelberg, Germany  \n3 Instituto de Física Teórica, UAM/CSIC, Universidad Autónoma de Madrid, Madrid, Spain  \n4 Institute of Theoretical Physics, Jagiellonian University, Krakow, Poland.  \n5 Quantum Science Center, Oak Ridge, TN 37931, USA  \n6 Information Sciences, Los Alamos National Laboratory, Los Alamos, NM 87545, USA  \narXiv :2 107 . 13470v2 [ quant-ph] 22 May 2023  \nError mitigation is an essential component of achieving a practical quantum advantage in the near term, and a number of different approaches have been proposed. In this work, we recognize that many state-of-theart error mitigation methods share a common feature: they are data-driven, employing classical data obtained from runs of different quantum circuits. For example, Zeronoise extrapolation (ZNE) uses variable noise data and Clifford-data regression (CDR) uses data from near-Clifford circuits. We show that Virtual Distillation (VD) can be viewed in a similar manner by considering classical data produced from different numbers of state preparations. Observing this fact allows us to unify these three methods under a general data-driven error mitigation framework that we call UNIfied Technique for Error mitigation with Data (UNITED) . In certain situations, we find that our UNITED method can outperform the individual methods (i.e., the whole is better than the individual parts) . Specifically, we employ a realistic noise model obtained from a trapped ion quantum computer to benchmark UNITED, as well as other state-of-the-art methods, in mitigating observables produced from random quantum circuits and the Quantum Alternating Operator Ansatz (QAOA) applied to Max-Cut problems with various numbers of qubits, circuit depthsand total numbers of shots. We find that the performance of different techniques depends strongly on shot budgets, with more powerful methods requiring more shots for optimal performance. For our largest considered shot budget (1010 ), we find that UNITED gives the most  \nDaniel Bultrini: The first two authors contributed equally to this work.  \nMax Hunter Gordon: The first two authors contributed equally to this work.  \naccurate mitigation. Hence, our work represents a benchmarking of current error mitigation methods and provides a guide for the regimes when certain methods are most useful.  \n1 Introduction  \nAs new generations of quantum computers become larger and less noisy, the practical application of quantum algorithms is just around the corner. It is likely that any quantum algorithm that can achieve an advantage over its classical counterpart will have to contend with substantial hardware errors. While quantum error correction may eventually allow one to eliminate hardware noise, doing so will require many more qubits and significantly lower error rates. Although full error correction will not be practical in the near term, it is possible to partially mitigate errors with far lower resource requirements. A number of such error mitigation techniques have been proposed [6, 14], each with its own benefits and trade-offs.  \nOne of the most popular approaches to error mitigation is zero noise extrapolation (ZNE) [45] . ZNE involves collecting data at different noise levels and fitting an extrapolation to the noiseless case. A variety of methods have been introduced to amplify the noise as well as to perform the extrapolation [13, 45] . ZNE has been shown to work well for small problem sizes [24] but has faced challenges for larger problems [10] . Due to the uncertainty of the extrapolation, ZNE does not have performance guarantees for r","cbCaifsmO9PNKNYR","https://ap.wps.com/l/cbCaifsmO9PNKNYR","pdf",2112689,25,"English","# Introduction\n## Key idea: unified data-driven error mitigation\n## Zero noise extrapolation (ZNE)\n## Clifford data regression (CDR)\n## Variable-noise CDR (vnCDR)\n## Virtual distillation (VD) and benchmarking","[{\"question\":\"What common feature do many state-of-the-art error mitigation methods share in this work?\",\"answer\":\"They are data-driven, relying on classical data obtained from runs of different quantum circuits.\"},{\"question\":\"How does the UNITED framework unify ZNE, CDR, and VD?\",\"answer\":\"It treats them as using classical data produced from different circuit conditions—such as noise scaling or varying numbers of state preparations—within one general data-driven mitigation framework.\"},{\"question\":\"What benchmarking setup is used to evaluate UNITED?\",\"answer\":\"A realistic noise model from a trapped-ion quantum computer benchmarks UNITED and other methods on observables from random circuits and QAOA for Max-Cut.\"},{\"question\":\"How do shot budgets affect the performance of different mitigation techniques?\",\"answer\":\"Performance depends strongly on shot budgets: more powerful methods require more shots for optimal results, and UNITED gives the most accurate mitigation at the largest studied budget.\"}]","Unifying and benchmarking state-of-the-art quantum error mitigation techniques - Research report | PDF",63]