[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-82137-en":3,"doc-seo-82137-105":29,"detail-sidebar-cat-0-en-105":90},{"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},82137,1099514067438,"River Wang","https://ap-avatar.wpscdn.com/avatar/100002539ee87300030?x-image-process=image/resize,m_fixed,w_180,h_180&k=1780474512215547542",8,"Research & Report","Shadow-Based Noise Fingerprinting of Simulated Quantum Noise Models","Accurate noise classification is crucial for operating near-term quantum processors, but quantum process tomography scales exponentially and hinders routine calibration. This work introduces a scalable noise fingerprinting pipeline using structured classical shadow tomography and physics-informed feature engineering from a fixed set of 3-qubit probe circuits. Each sample is mapped to a 279-dimensional Pauli-shadow and derived observable feature vector to separate physically similar channels. Experiments on 14,000 labeled samples spanning 10 noise types show random forest reaches 0.8426 test accuracy and macro F1 of 0.8437.","Shadow-Based Noise Fingerprinting of Simulated  \nQuantum Noise Models  \nVridhi Jain  \nDepartment of Quantum Science and Engineering University of Delaware DE, USA [vridhi@udel.edu](vridhi@udel.edu)  \nLei Zhang  \nDepartment of Information Systems University of Maryland, Baltimore County MD, USA [leizhang@umbc.edu](leizhang@umbc.edu)  \narXiv :2607 .08998v 1 [ cs . SE] 10 Jul 2026  \nAbstract—Accurate noise classification is essential for operating near-term quantum processors, yet existing approaches, such as quantum process tomography, scale exponentially with system size, limiting their practicality for routine calibration. We propose a scalable noise fingerprinting pipeline that combines structured classical shadow tomography with physics-informed feature engineering to identify noise channels from a fixed set of 3-qubit probe circuits. Each sample is represented by a 279-dimensional feature vector constructed from randomized Pauli measurements and derived observables, designed to resolve physically similar noise channels that produce overlapping signatures under generic measurement sets. We evaluate three classifiers, i.e., random forest, extra trees, and a multilayer perceptron, on a dataset of 14,000 labeled samples spanning 10 noise types. The random forest classifier achieves the highest test accuracy of 0.8426 with a macro F1 score of 0.8437, outperforming both baselines. Confusion analysis reveals that many noise types are classified with high reliability, with the remaining confusions occurring between channels sharing similar physical decay mechanisms, motivating future work on richer probe states and noise parameter estimation.  \nIndex Terms—quantum noise classification, classical shadow tomography, noise fingerprinting, machine learning  \nI. INTRODUCTION  \nQuantum processors based on superconducting qubits and trapped ions are rapidly advancing toward practical applications, yet their performance remains fundamentally constrained by hardware noise [1] . Noise in these systems arises from a variety of physical mechanisms, including energy relaxation, dephasing, and measurement errors, each of which degrades quantum information in distinct ways. Understanding the dominant noise sources in a given device is therefore a prerequisite for effective error mitigation and the design of noise-aware quantum algorithms.  \nExisting approaches such as quantum process tomography [2] provide complete noise classification but require resources that scale exponentially with system size, making them impractical for routine calibration of large-scale devices. Direct fidelity estimation and randomized benchmarking offer partial improvements in scalability, yet remain limited to specific noise models or rely on hardcoded decision rules that do not generalize across diverse error channels. As processors scale in qubit count and circuit depth, efficient and generalizable noise identification becomes increasingly critical [1] .  \nIn this work, we address this gap by proposing a noise fingerprinting pipeline 1 that exploits the efficiency of classical shadow tomography [3] to extract structured measurement data from a small set of 3-qubit probe circuits [4] . Rather than relying on generic observables, we construct a physicsinformed feature representation designed to amplify distinctions between physically similar noise channels. We benchmark three machine learning classifiers on ten candidate noise models and demonstrate that ensemble methods substantially outperform a neural baseline, achieving over 0.84 test accuracy on a dataset of 14,000 labeled samples.  \nOur contributions are twofold. First, the paper proposesa shadow-based noise fingerprinting pipeline using fixed 3-qubit probe circuits with a 279-dimensional physics-informed feature representation combining Pauli-shadow observablesand derived coherence/population/asymmetry features. Second, we propose an empirical evaluation across ten Qiskit noise models and three classifiers, incl","cbCaigMfCuJeZ4QV","https://ap.wps.com/l/cbCaigMfCuJeZ4QV","pdf",631700,1,4,"English","en",105,"# Introduction\n# Background and Related Work\n## Noise models\n## Shadow tomography\n## Prior methods","[{\"question\":\"Why is noise classification important for near-term quantum processors?\",\"answer\":\"Noise degrades quantum information and limits device performance. Identifying dominant noise sources is a prerequisite for error mitigation and designing noise-aware quantum algorithms.\"},{\"question\":\"What pipeline does the paper propose for noise fingerprinting?\",\"answer\":\"The method combines structured classical shadow tomography with physics-informed feature engineering. It uses a fixed set of 3-qubit probe circuits and represents each sample as a 279-dimensional feature vector derived from randomized Pauli measurements and observables.\"},{\"question\":\"How well do the classifiers perform, and which one is best?\",\"answer\":\"Three classifiers are benchmarked: random forest, extra trees, and a multilayer perceptron. Random forest achieves the best results with 0.8426 test accuracy and a macro F1 score of 0.8437 on 14,000 labeled samples across 10 noise types.\"}]",1784178395,10,{"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":85,"head_meta":87,"extra_data":89,"updated_unix":27},"shadow-based-noise-fingerprinting-of-simulated-quantum-noise-models","",{"@graph":35,"@context":84},[36,52,67],{"@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":21},"https://docshare.wps.com/document/shadow-based-noise-fingerprinting-of-simulated-quantum-noise-models/82137/",{"url":51,"name":13,"@type":53,"author":54,"headline":13,"publisher":56,"fileFormat":59,"inLanguage":23,"description":14,"dateModified":60,"datePublished":61,"encodingFormat":59,"isAccessibleForFree":62,"interactionStatistic":63},"DigitalDocument",{"name":9,"@type":55},"Person",{"url":40,"name":57,"@type":58},"DocShare","Organization","application/pdf","2026-07-22","2026-07-16",true,{"@type":64,"interactionType":65,"userInteractionCount":20},"InteractionCounter",{"@type":66},"ViewAction",{"@type":68,"mainEntity":69},"FAQPage",[70,76,80],{"name":71,"@type":72,"acceptedAnswer":73},"Why is noise classification important for near-term quantum processors?","Question",{"text":74,"@type":75},"Noise degrades quantum information and limits device performance. Identifying dominant noise sources is a prerequisite for error mitigation and designing noise-aware quantum algorithms.","Answer",{"name":77,"@type":72,"acceptedAnswer":78},"What pipeline does the paper propose for noise fingerprinting?",{"text":79,"@type":75},"The method combines structured classical shadow tomography with physics-informed feature engineering. It uses a fixed set of 3-qubit probe circuits and represents each sample as a 279-dimensional feature vector derived from randomized Pauli measurements and observables.",{"name":81,"@type":72,"acceptedAnswer":82},"How well do the classifiers perform, and which one is best?",{"text":83,"@type":75},"Three classifiers are benchmarked: random forest, extra trees, and a multilayer perceptron. Random forest achieves the best results with 0.8426 test accuracy and a macro F1 score of 0.8437 on 14,000 labeled samples across 10 noise types.","https://schema.org",{"og:url":51,"og:type":86,"og:title":13,"og:site_name":57,"og:description":14},"article",{"robots":88,"canonical":51},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":91},[92,96,100,104,109,114,119,122,127,130,133],{"id":20,"doc_module":4,"doc_module_name":45,"category_name":93,"show_sort_weight":94,"slug":95},"Story & Novel",90,"story-novel",{"id":46,"doc_module":4,"doc_module_name":45,"category_name":97,"show_sort_weight":98,"slug":99},"Literature",80,"literature",{"id":21,"doc_module":4,"doc_module_name":45,"category_name":101,"show_sort_weight":102,"slug":103},"Exam",70,"exam",{"id":105,"doc_module":4,"doc_module_name":45,"category_name":106,"show_sort_weight":107,"slug":108},5,"Comic",60,"comic",{"id":110,"doc_module":4,"doc_module_name":45,"category_name":111,"show_sort_weight":112,"slug":113},6,"Technology",50,"technology",{"id":115,"doc_module":4,"doc_module_name":45,"category_name":116,"show_sort_weight":117,"slug":118},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":45,"category_name":12,"show_sort_weight":120,"slug":121},30,"research-report",{"id":123,"doc_module":4,"doc_module_name":45,"category_name":124,"show_sort_weight":125,"slug":126},9,"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":45,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":28,"doc_module":4,"doc_module_name":45,"category_name":131,"show_sort_weight":28,"slug":132},"Lifestyle","lifestyle",{"id":134,"doc_module":4,"doc_module_name":45,"category_name":135,"show_sort_weight":105,"slug":136},19,"General","general"]