[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-128226-en":3,"doc-seo-128226-105":31,"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":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},128226,2336475104362,"Eden","https://ap-avatar.wpscdn.com/avatar/22000c4c46a41b752dd?x-image-process=image/resize,m_fixed,w_180,h_180&k=1786595829695023868",8,"Research & Report","Mitigating Noise in Quantum Software Testing Using Machine Learning","Quantum software testing for validating correctness becomes unreliable on NISQ devices because hardware noise can mask real faults, making it unclear whether failures are caused by noise or defects. An ML-based, noise-aware approach named QOIN is proposed to learn a quantum computer’s noise behavior and filter it from program outputs, then assess test cases using an oracle. Experiments on IBM, Google, and Rigetti setups with real and artificial programs show QOIN reduces noise impact by over 80% for most noise models. It achieves strong precision, recall, and F1 scores across tested oracles.","MITIGATING NOISE IN QUANTUM SOFTWARE TESTING USING  \nMACHINE LEARNING  \narXiv :2306 . 16992v2 [ cs . SE] 15 Jan 2024  \nAsmar Muqeet  \nSimula Research Laboratory University of Oslo Oslo [asmar@simula.no](asmar@simula.no)  \nTao Yue  \nSimula Research Laboratory Oslo [taoyue@gmail.com](taoyue@gmail.com)  \nShaukat Ali  \nSimula Research Laboratory and Oslo Metropolitan University Oslo [shaukat@simula.no](shaukat@simula.no)  \nPaolo Arcaini  \nNational Institute of Informatics  \nTokyo  \n[arcaini@nii.ac.jp](arcaini@nii.ac.jp)  \nABSTRACT  \nQuantum Computing (QC) promises computational speedup over classic computing for solving complex problems. However, noise exists in current and near-term quantum computers. Quantum software testing (for gaining confidence in quantum software’s correctness) is inevitably impacted by noise, to the extent that it is impossible to know if a test case failed due to noise or real faults. Existing testing techniques test quantum programs without considering noise, i.e., by executing tests on ideal quantum computer simulators. Consequently, they are not directly applicable to testing quantum software on real quantum computers or noisy simulators. To this end, we propose a noise-aware approach (named QOIN) to alleviate the noise effect on test results of quantum programs. QOIN employs machine learning techniques (e.g., transfer learning) to learn the noise effect of a quantum computer and filter it from a quantum program’s outputs. Such filtered outputs are then used as the input to perform test case assessments (determining the passing or failing of a test case execution against a test oracle) . We evaluated QOIN on IBM’s 23 noise models, Google’s two available noise models, and Rigetti’s Quantum Virtual Machine (QVM), with nine real-world quantum programs and 1000 artificial quantum programs. We also generated faulty versions of these programs to check if a failing test case execution can be determined under noise. Results show that QOIN can reduce the noise effect by more than 80% on the majority of noise models. To check QOIN’s effectiveness for quantum software testing, we used an existing test oracle for quantum software testing.  \nThe results showed that QOIN attained scores of 99%, 75%, and 86% for precision, recall, and F1-score, respectively, for the test oracle across six real-world programs. For artificial programs, QOIN achieved scores of 93%, 79%, and 86% for precision, recall, and F1-score. This highlights QOIN’s effectiveness in learning noise patterns for noise-aware quantum software testing.  \nKeywords Software and its engineering · Software testing and debugging · Computing methodologies · Instancebased learning · Quantum Computing · Machine learning.  \n1 Introduction  \nThere has been an increased interest in quantum software engineering over the past few years, focusing on designing, developing, and testing quantum computing (QC) applications [1, 2, 3, 4, 5, 6, 7, 8] . This growth of interest is due to the computational power promised by quantum computers to solve a particular class of problems more efficiently than classic computers [9] . In addition, quantum computers (IBM [10], Google [11], Rigetti [12]), and quantum computer simulators such as QuEST [13], QX [14], and IBM’s Qiskit Aer simulator [15] are becoming available. However, quantum computers are susceptible to hardware noise due to immature hardware and environmental factors (e.g.,  \nQOIN  \nmagnetic fields, radiations) [16, 17] . Noise affects the accuracy of calculations a quantum computer performs, thus resulting in incorrect program outputs. Such computers with inherent noise are known as Noisy Intermediate-Scale Quantum (NISQ) computers [18] .  \nQuantum software testing aims to cost-effectively find quantum software bugs to achieve a certain level of confidence in their correctness [19, 20, 21, 22, 1] . Testing quantum software is challenging due to the inherent quantum mechanics’ features, such as superposition and entanglem","cbCaic3uYJ0GXg00","https://ap.wps.com/l/cbCaic3uYJ0GXg00","pdf",1738831,2,1,24,"English","en",105,"# Abstract\n# Introduction\n## Background: quantum software testing and NISQ noise\n## Problem: uncertainty between faults and noise\n## Proposed solution: QOIN noise-aware testing","[{\"question\":\"Why does noise complicate quantum software testing on NISQ computers?\",\"answer\":\"Noise can alter quantum program outputs, making it difficult to determine whether a test failure is due to genuine program faults or noise effects.\"},{\"question\":\"What is QOIN and what does it do?\",\"answer\":\"QOIN is a noise-aware testing approach that uses machine learning to learn the noise effect of a quantum computer, filters it from quantum outputs, and then applies a test oracle to judge pass/fail.\"},{\"question\":\"How effective is QOIN according to the reported evaluation results?\",\"answer\":\"QOIN reduces the noise effect by more than 80% for most noise models and achieves high precision, recall, and F1-score performance on both real-world and artificial programs.\"}]","Mitigating Noise in Quantum Software Testing Using Machine Learning | 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does noise complicate quantum software testing on NISQ computers?","Question",{"text":76,"@type":77},"Noise can alter quantum program outputs, making it difficult to determine whether a test failure is due to genuine program faults or noise effects.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"What is QOIN and what does it do?",{"text":81,"@type":77},"QOIN is a noise-aware testing approach that uses machine learning to learn the noise effect of a quantum computer, filters it from quantum outputs, and then applies a test oracle to judge pass/fail.",{"name":83,"@type":74,"acceptedAnswer":84},"How effective is QOIN according to the reported evaluation results?",{"text":85,"@type":77},"QOIN reduces the noise effect by more than 80% for most noise models and achieves high precision, recall, and F1-score performance on both real-world and artificial 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