[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-85483-en":3,"doc-seo-85483-105":29,"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":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":13,"seo_description":14,"update_tm":27,"read_time":28},85483,962075006959,"Anda","https://ap-avatar.wpscdn.com/avatar/e0002397efbe92a78e?_k=1776741047341049297",8,"Research & Report","Toward Live Noise Fingerprinting in Quantum Software Engineering","Contemporary quantum computers introduce inherent hardware noise that severely affects quantum software development, testing, and debugging. Simplified or outdated noise assumptions can misjudge program correctness, hide faults, and limit portability across platforms. Traditional reconstruction-based noise estimation is computationally expensive and quickly becomes outdated as hardware changes. This vision paper introduces SIMSHADOW, a classical shadow tomography-based pipeline for efficient, continuously updatable noise fingerprinting from empirical observations. Experiments with Qiskit and Cirq on IBM Boston and Quantinuum H2 show channel-specific structure, interpretable heatmaps, and quantified cross-platform discrepancies.","Toward Live Noise Fingerprinting in Quantum  \nSoftware Engineering  \nAvner Bensoussan King’s College London, UK  \nElena Chachkarova King’s College London, UK  \nKarine Even-Mendoza King’s College London, UK  \nSophie Fortz  \nInria, Univ. Rennes, CNRS, IRISA, France  \nVasileios Klimis  \nQueen Mary University of London, UK  \nMohammad Reza Mousavi King’s College London, UK  \narXiv :2512 . 18667v3 [ quant-ph] 10 Jul 2026  \nAbstract—Contemporary quantum computers are inherently noisy, posing significant challenges for the development and testing of quantum software. Simplified or outdated noise assumptions can lead to incorrect assessments of program correctness, obscure debugging, and hinder cross-platform portability, creating a critical quantum software development gap. Providing accurate, practical noise characterisation is challenging as traditional reconstruction methods scale exponentially and rapidly become outdated.  \nIn this vision paper, we address this gap via a novel classical shadow tomography-based pipeline, SIMSHADOW, enabling efficient, continuously updatable noise fingerprinting from empirical observations, suitable for integration into quantum software development workflows, including testing and validation. We prototyped the pipeline to investigate fingerprints’ ability to capture structured, interpretable noise and cross-platform discrepancies affecting quantum programs’ behaviour to support realistic testing and debugging in future tools. Our evaluation with Qiskit and Cirq under widely used hardware-informed profiles, IBM Boston and Quantinuum H2, shows fingerprints exhibit channel-specific structure and yield interpretable heatmaps. We observed systematic cross-platform discrepancies under matched noise configurations, quantified by large Frobenius distances at a fraction of full tomography cost. On 69 MQTBENCH programs, larger fingerprint differences correlate with output distributions divergences, highlighting threats for testing and cross-platform debugging tasks.  \nIndex Terms—QSE, Noise Models, Cross-Platform Validation  \nI. INTRODUCTION  \nIn contemporary Quantum Software Engineering (QSE) [1],[2], activities such as development, compilation and debugging face a unique obstacle: sensitivity to the “noise” of quantum hardware. Quantum programs are affected by fragile qubits whose behaviour is disrupted by environmental fluctuations and imperfect control [3]–[6] . In quantum ecosystems (e.g. Qiskit or Cirq), simulators are indispensable for QSE, providing controlled environments for a variety of tasks and enabling low-cost experimentation [7], [8] . Simulators such as those for Qiskit [9] and Cirq [10] must capture not only the ideal logic of algorithms but also the noisy, platformspecific imperfections of real hardware [3], [11], [12] . These imperfections are abstracted into noise models, which are often inaccurate and out-of-sync with respect to real hardware [13],[14] . Recent work has focused on improving these models  \n[13], highlighting their central importance for reliable and effective QSE [15]–[18] .  \nYet, a critical conceptual gap exists: there is a significant lack of accurate and practically actionable noise models. Although platforms offer typical noise channels (e.g.“depolarising noise”), the underlying semantics of their implementations differ significantly. This undermines the role of simulators in QSE for evaluating quantum software behaviour in noisy environments and represents a fundamental, unaddressed challenge for building reliable and transferable quantum software.  \nWe propose efficient noise fingerprinting as a new vision for addressing this challenge. We adapt classical shadow tomography [19], a lightweight quantum mechanics technique, as a software engineering analysis tool to generate rich, descriptive signatures of a simulator’s noise profile, thereby avoiding the exponential cost of accurately reconstructing the quantum state under noise. We investigate this paradigm empirica","cbCaikZxnKpUGhrh","https://ap.wps.com/l/cbCaikZxnKpUGhrh","pdf",808909,1,7,"English","en",105,"# Introduction\n# Background & Related Work","[{\"question\":\"Why do inaccurate noise models hinder quantum software engineering?\",\"answer\":\"Because quantum programs are sensitive to hardware imperfections, outdated or oversimplified noise assumptions can lead to incorrect correctness assessments, obscure debugging signals, and reduce portability across platforms.\"},{\"question\":\"What is SIMSHADOW and how does it improve noise characterization?\",\"answer\":\"SIMSHADOW is a pipeline that uses classical shadow tomography to generate efficient, descriptive noise fingerprints from empirical observations, avoiding the exponential cost of full reconstruction while staying continuously updatable.\"},{\"question\":\"How does SIMSHADOW support testing and debugging across platforms?\",\"answer\":\"The pipeline produces channel-specific fingerprints and interpretable heatmaps, and it quantifies systematic cross-platform discrepancies even when noise configurations are matched, helping differential testing and automated testing under realistic noise.\"}]",1784203924,18,{"code":4,"msg":30,"data":31},"ok",{"site_id":24,"language":23,"slug":32,"title":13,"keywords":33,"description":14,"schema_data":34,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":27},"toward-live-noise-fingerprinting-in-quantum-software-engineering","",{"@graph":35,"@context":85},[36,53,68],{"@type":37,"itemListElement":38},"BreadcrumbList",[39,43,47,50],{"item":40,"name":41,"@type":42,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":44,"name":45,"@type":42,"position":46},"https://docshare.wps.com/document/","Document",2,{"item":48,"name":12,"@type":42,"position":49},"https://docshare.wps.com/document/research-report/",3,{"item":51,"name":13,"@type":42,"position":52},"https://docshare.wps.com/document/toward-live-noise-fingerprinting-in-quantum-software-engineering/85483/",4,{"url":51,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":23,"description":14,"dateModified":61,"datePublished":62,"encodingFormat":60,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":40,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-07-17","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},"Why do inaccurate noise models hinder quantum software engineering?","Question",{"text":75,"@type":76},"Because quantum programs are sensitive to hardware imperfections, outdated or oversimplified noise assumptions can lead to incorrect correctness assessments, obscure debugging signals, and reduce portability across platforms.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What is SIMSHADOW and how does it improve noise characterization?",{"text":80,"@type":76},"SIMSHADOW is a pipeline that uses classical shadow tomography to generate efficient, descriptive noise fingerprints from empirical observations, avoiding the exponential cost of full reconstruction while staying continuously updatable.",{"name":82,"@type":73,"acceptedAnswer":83},"How does SIMSHADOW support testing and debugging across platforms?",{"text":84,"@type":76},"The pipeline produces channel-specific fingerprints and interpretable heatmaps, and it quantifies systematic cross-platform discrepancies even when noise 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