Enhancing Quantum Computation Accuracy on Ibm Quantum Hardware Through Advanced Error Mitigation Strategies

Enhancing Quantum Computation Accuracy on Ibm Quantum Hardware Through Advanced Error Mitigation Strategies | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Enhancing Quantum Computation Accuracy on Ibm Quantum Hardware Through Advanced Error Mitigation Strategies Lakshanya D, Nandha Kumar P, V. Karthick This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7846267/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 11 You are reading this latest preprint version Abstract Quantum computing has recently emerged as a novel paradigm for solving problems otherwise intractable on classical computers, yet the practical progress of quantum computers is severely impacted by noise, decoherence, and imperfect gate operations in Noisy Intermediate-Scale Quantum (NISQ) devices. Specific to IBM Quantum hardware, imperfections due to readout error, gate errors, and device drift, which depends on time, resolves limits for the accuracy of outputs from our algorithms. The mitigation strategies currently used, for example, Measurement Error Mitigation (MEM), Zero Noise Extrapolation (ZNE), and Clifford Data Regression (CDR), offer improvement over raw, unmitigated outputs, but they are static and not dynamic; the reduction of noise in within an NISQ device is temporal and can vary in patterns that are non-zero. In addition to this, using the conventional methods provides inconsistent and suboptimal results. This study will provide enhanced reliability with respect to performing quantum computation on IBM Quantum hardware with a unique mitigation framework that is adaptive and computationally efficient for a high-fidelity output. The problem taken on here is the failure of existing error mitigation strategies to provide adaptive noise compensation, as they assume a statically-distributed error, despite the performance-related characteristics already being documented in regards to calibration drift and variation over time. To achieve this, we propose a Hybrid Adaptive Error Mitigation (HAEM) approach that includes baseline measurement error mitigation and an additional correction layer that utilizes realtime calibration information, along with lightweight machine learning models. The approach can be broken down into three steps: (i) perform standard measurement error mitigation to reduce classical readout noise, (ii) execute compact calibration circuits, such as Bell states, GHZ states and Clifford benchmarks to capture then current error profile of the device, and (iii) use a lightweight machine learning model (having been trained on historical and newest calibration data) to dynamically change the mitigation weights applied for the target quantum algorithm. All implementation is done using Qiskit Runtime to ensure low-latency execution, and we completed experiments on both Qiskit Aer noisy simulators and IBM Quantum devices. Initial results suggest HAEM improved the outputs of quantum algorithms' fidelity compared to the unmitigated runs and the baseline measurement error mitigation (MEM) runs. On the noisy simulators, HAEM increased fidelity from a raw performance of 0.65 to 0.87, nearly a 34% improvement. In fairer hardware-like scenarios, fidelity-on-average was persistently 12% better than MEM, with nearly equal time duration for the additional adaptive measurements. The adaptive aspect of HAEM consistently supported subsequent calibration cycles of the device and was effective to continue providing fidelity improvements despite the static mitigation performance degrading. To sum up, the proposed HAEM framework is a modern and viable alternative for error mitigation which brings together the benefits of baseline approaches with corrections that are driven by fixed-function error-corrective, adaptive learning. As both learning-based and standard baseline approaches can only achieve fixed levels of performance, HAEM, through its use of noise mitigation both at execution time and through correlating it with standard data, provides a route towards reliable quantum computations on current IBM hardware. HAEM also presents a contribution to the NISQ error-resilient landscape, and however it is intended that it will continue to improve future applications of hybrid workflows of quantum and classical computation where error mitigation and resilience is important. Quantum Error Mitigation Hardware Computing Noise HAEM Hybrid Adaptive Circuit Noisy MEM ZNE Errors Accuracy Intermediate Scale Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 30 Dec, 2025 Reviews received at journal 16 Dec, 2025 Reviews received at journal 15 Dec, 2025 Reviewers agreed at journal 21 Nov, 2025 Reviewers agreed at journal 21 Nov, 2025 Reviewers agreed at journal 11 Nov, 2025 Reviewers invited by journal 06 Nov, 2025 Editor assigned by journal 02 Nov, 2025 Editor invited by journal 31 Oct, 2025 Submission checks completed at journal 24 Oct, 2025 First submitted to journal 24 Oct, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7846267","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":545831571,"identity":"4058c341-6bf8-4a17-b9dc-16460fc3c768","order_by":0,"name":"Lakshanya D","email":"data:image/png;base64,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","orcid":"","institution":"Anna University","correspondingAuthor":true,"prefix":"","firstName":"Lakshanya","middleName":"","lastName":"D","suffix":""},{"id":545831572,"identity":"f99d92d8-cdd2-45d5-a8e4-0864baa71d29","order_by":1,"name":"Nandha Kumar P","email":"","orcid":"","institution":"Anna University","correspondingAuthor":false,"prefix":"","firstName":"Nandha","middleName":"Kumar","lastName":"P","suffix":""},{"id":545831573,"identity":"46961be6-2596-429c-9410-fb7ec570bb0f","order_by":2,"name":"V. 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Specific to IBM Quantum hardware, imperfections due to readout error, gate errors, and device drift, which depends on time, resolves limits for the accuracy of outputs from our algorithms. The mitigation strategies currently used, for example, Measurement Error Mitigation (MEM), Zero Noise Extrapolation (ZNE), and Clifford Data Regression (CDR), offer improvement over raw, unmitigated outputs, but they are static and not dynamic; the reduction of noise in within an NISQ device is temporal and can vary in patterns that are non-zero. In addition to this, using the conventional methods provides inconsistent and suboptimal results.\u003c/p\u003e\u003cp\u003eThis study will provide enhanced reliability with respect to performing quantum computation on IBM Quantum hardware with a unique mitigation framework that is adaptive and computationally efficient for a high-fidelity output. The problem taken on here is the failure of existing error mitigation strategies to provide adaptive noise compensation, as they assume a statically-distributed error, despite the performance-related characteristics already being documented in regards to calibration drift and variation over time.\u003c/p\u003e\u003cp\u003eTo achieve this, we propose a Hybrid Adaptive Error Mitigation (HAEM) approach that includes baseline measurement error mitigation and an additional correction layer that utilizes realtime calibration information, along with lightweight machine learning models. The approach can be broken down into three steps: (i) perform standard measurement error mitigation to reduce classical readout noise, (ii) execute compact calibration circuits, such as Bell states, GHZ states and Clifford benchmarks to capture then current error profile of the device, and (iii) use a lightweight machine learning model (having been trained on historical and newest calibration data) to dynamically change the mitigation weights applied for the target quantum algorithm. All implementation is done using Qiskit Runtime to ensure low-latency execution, and we completed experiments on both Qiskit Aer noisy simulators and IBM Quantum devices.\u003c/p\u003e\u003cp\u003eInitial results suggest HAEM improved the outputs of quantum algorithms' fidelity compared to the unmitigated runs and the baseline measurement error mitigation (MEM) runs. On the noisy simulators, HAEM increased fidelity from a raw performance of 0.65 to 0.87, nearly a 34% improvement. In fairer hardware-like scenarios, fidelity-on-average was persistently 12% better than MEM, with nearly equal time duration for the additional adaptive measurements. The adaptive aspect of HAEM consistently supported subsequent calibration cycles of the device and was effective to continue providing fidelity improvements despite the static mitigation performance degrading.\u003c/p\u003e\u003cp\u003eTo sum up, the proposed HAEM framework is a modern and viable alternative for error mitigation which brings together the benefits of baseline approaches with corrections that are driven by fixed-function error-corrective, adaptive learning. As both learning-based and standard baseline approaches can only achieve fixed levels of performance, HAEM, through its use of noise mitigation both at execution time and through correlating it with standard data, provides a route towards reliable quantum computations on current IBM hardware. HAEM also presents a contribution to the NISQ error-resilient landscape, and however it is intended that it will continue to improve future applications of hybrid workflows of quantum and classical computation where error mitigation and resilience is important.\u003c/p\u003e","manuscriptTitle":"Enhancing Quantum Computation Accuracy on Ibm Quantum Hardware Through Advanced Error Mitigation Strategies","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-18 17:32:25","doi":"10.21203/rs.3.rs-7846267/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-12-30T09:03:35+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-16T21:38:22+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-16T04:59:09+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"281580383095054599015491513629267694407","date":"2025-11-21T18:03:03+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"188150012055722457515767551186493162827","date":"2025-11-21T17:35:45+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"327632568447631504858373035093213018882","date":"2025-11-11T17:03:59+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-11-06T16:33:06+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-11-02T17:13:27+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-10-31T11:03:30+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-10-24T14:09:57+00:00","index":"","fulltext":""},{"type":"submitted","content":"Discover Quantum Science","date":"2025-10-24T14:07:09+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"discover-quantum-science","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Discover Quantum Science](https://link.springer.com/journal/44464)","snPcode":"44464","submissionUrl":"https://submission.nature.com/new-submission/44464/3","title":"Discover Quantum Science","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Open","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"49711204-1d84-4d25-8215-c99e0e5ab168","owner":[],"postedDate":"November 18th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-03T13:09:22+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-18 17:32:25","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7846267","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7846267","identity":"rs-7846267","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}