{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,25]],"date-time":"2026-04-25T16:22:10Z","timestamp":1777134130641,"version":"3.51.4"},"reference-count":53,"publisher":"Association for Computing Machinery (ACM)","issue":"5","funder":[{"name":"Qu-Test project","award":["299827"],"award-info":[{"award-number":["299827"]}]},{"name":"Research Council of Norway and Simula\u2019s internal strategic project on quantum software engineering"},{"name":"Simula\u2019s internal strategic project on quantum software engineering."},{"name":"Oslo Metropolitan University\u2019s"},{"DOI":"10.13039\/100024160","name":"ASPIRE","doi-asserted-by":"crossref","award":["JPMJAP2301, JST."],"award-info":[{"award-number":["JPMJAP2301, JST."]}],"id":[{"id":"10.13039\/100024160","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Softw. Eng. Methodol."],"published-print":{"date-parts":[[2026,5,31]]},"abstract":"<jats:p>\n                    With the rapid advancement of quantum computing, research on quantum machine learning (QML) algorithms has grown significantly. Among these, the Quantum Neural Network (QNN) stands out as one of the promising algorithms that integrates the principles of quantum computing with artificial neural networks to process data. Inspired by applications of QNN across fields, we investigate their use in software testing for the Cancer Registry of Norway (CRN), part of the Norwegian Institute of Public Health (NIPH), responsible for cancer statistics among the Norwegian population. CRN develops a complex socio-technical software system, Cancer Registration Support System (\n                    <jats:inline-formula content-type=\"math\/tex\">\n                      <jats:tex-math notation=\"LaTeX\" version=\"MathJax\">\\(\\mathtt{CaReSS}\\)<\/jats:tex-math>\n                    <\/jats:inline-formula>\n                    ), interacting with many entities (e.g., hospitals, medical laboratories, and other patient registries) to achieve its task. For cost-effective testing of\n                    <jats:inline-formula content-type=\"math\/tex\">\n                      <jats:tex-math notation=\"LaTeX\" version=\"MathJax\">\\(\\mathtt{CaReSS}\\)<\/jats:tex-math>\n                    <\/jats:inline-formula>\n                    , CRN has employed\n                    <jats:inline-formula content-type=\"math\/tex\">\n                      <jats:tex-math notation=\"LaTeX\" version=\"MathJax\">\\(\\mathtt{EvoMaster}\\)<\/jats:tex-math>\n                    <\/jats:inline-formula>\n                    , an AI-based REST API testing tool combined with an integrated classical machine learning model\n                    <jats:inline-formula content-type=\"math\/tex\">\n                      <jats:tex-math notation=\"LaTeX\" version=\"MathJax\">\\(\\mathtt{EvoClass}\\)<\/jats:tex-math>\n                    <\/jats:inline-formula>\n                    . Within this context, we propose\n                    <jats:inline-formula content-type=\"math\/tex\">\n                      <jats:tex-math notation=\"LaTeX\" version=\"MathJax\">\\(\\mathtt{EvoQlass}\\)<\/jats:tex-math>\n                    <\/jats:inline-formula>\n                    to investigate the feasibility of using, inside\n                    <jats:inline-formula content-type=\"math\/tex\">\n                      <jats:tex-math notation=\"LaTeX\" version=\"MathJax\">\\(\\mathtt{EvoMaster}\\)<\/jats:tex-math>\n                    <\/jats:inline-formula>\n                    , a QNN classifier, instead of the existing classical machine learning model. Results indicate that\n                    <jats:inline-formula content-type=\"math\/tex\">\n                      <jats:tex-math notation=\"LaTeX\" version=\"MathJax\">\\(\\mathtt{EvoQlass}\\)<\/jats:tex-math>\n                    <\/jats:inline-formula>\n                    can achieve performance comparable to that of\n                    <jats:inline-formula content-type=\"math\/tex\">\n                      <jats:tex-math notation=\"LaTeX\" version=\"MathJax\">\\(\\mathtt{EvoClass}\\)<\/jats:tex-math>\n                    <\/jats:inline-formula>\n                    . We further explore the effects of various QNN configurations on performance and offer recommendations for optimal QNN settings for future QNN developers.\n                  <\/jats:p>","DOI":"10.1145\/3769302","type":"journal-article","created":{"date-parts":[[2025,9,29]],"date-time":"2025-09-29T15:52:22Z","timestamp":1759161142000},"page":"1-24","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":2,"title":["Quantum Neural Network Classifier for Cancer Registry System Testing: A Feasibility Study"],"prefix":"10.1145","volume":"35","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5621-6140","authenticated-orcid":false,"given":"Xinyi","family":"Wang","sequence":"first","affiliation":[{"name":"Simula Research Laboratory, Oslo, Norway and University of Oslo, Oslo, Norway"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9979-3519","authenticated-orcid":false,"given":"Shaukat","family":"Ali","sequence":"additional","affiliation":[{"name":"Simula Research Laboratory, Oslo, Norway and Oslo Metropolitan University, Oslo, Norway"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6253-4062","authenticated-orcid":false,"given":"Paolo","family":"Arcaini","sequence":"additional","affiliation":[{"name":"National Institute of Informatics, Chiyoda-ku, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4875-9708","authenticated-orcid":false,"given":"Narasimha Raghavan","family":"Veeraragavan","sequence":"additional","affiliation":[{"name":"Cancer Registry of Norway, Oslo, Norway and Norwegian Institute of Public Health, Oslo, Norway"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9655-7003","authenticated-orcid":false,"given":"Jan F.","family":"Nyg\u00e5rd","sequence":"additional","affiliation":[{"name":"Cancer Registry of Norway, Oslo, Norway and UiT The Arctic University of Norway, Tromso, Norway"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2026,4,24]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"publisher","DOI":"10.1038\/s43588-021-00084-1"},{"key":"e_1_3_2_3_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICST.2018.00046"},{"key":"e_1_3_2_4_2","doi-asserted-by":"publisher","DOI":"10.1145\/3293455"},{"key":"e_1_3_2_5_2","doi-asserted-by":"publisher","DOI":"10.21105\/joss.02153"},{"key":"e_1_3_2_6_2","doi-asserted-by":"publisher","DOI":"10.1007\/s42484-022-00062-4"},{"key":"e_1_3_2_7_2","doi-asserted-by":"publisher","DOI":"10.1140\/epjqt\/s40507-021-00114-x"},{"key":"e_1_3_2_8_2","doi-asserted-by":"publisher","DOI":"10.1103\/RevModPhys.94.015004"},{"key":"e_1_3_2_9_2","doi-asserted-by":"publisher","DOI":"10.1007\/JHEP02(2021)212"},{"key":"e_1_3_2_10_2","doi-asserted-by":"publisher","DOI":"10.1140\/epjqt\/s40507-021-00091-1"},{"key":"e_1_3_2_11_2","doi-asserted-by":"publisher","DOI":"10.1038\/s43588-022-00311-3"},{"key":"e_1_3_2_12_2","doi-asserted-by":"publisher","DOI":"10.1007\/s42484-023-00106-3"},{"key":"e_1_3_2_13_2","doi-asserted-by":"publisher","DOI":"10.1007\/s42484-022-00093-x"},{"key":"e_1_3_2_14_2","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevApplied.21.067001"},{"key":"e_1_3_2_15_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41586-019-0980-2"},{"key":"e_1_3_2_16_2","doi-asserted-by":"publisher","DOI":"10.1109\/IJCNN60899.2024.10651123"},{"key":"e_1_3_2_17_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICSME58846.2023.00065"},{"key":"e_1_3_2_18_2","doi-asserted-by":"publisher","DOI":"10.1080\/09720502.2020.1731948"},{"key":"e_1_3_2_19_2","doi-asserted-by":"publisher","DOI":"10.1007\/s42452-020-2847-4"},{"key":"e_1_3_2_20_2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2024.3382150"},{"key":"e_1_3_2_21_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICUFN49451.2021.9528698"},{"key":"e_1_3_2_22_2","doi-asserted-by":"publisher","DOI":"10.1145\/3611643.3613882"},{"key":"e_1_3_2_23_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICSE-Companion58688.2023.00102"},{"key":"e_1_3_2_24_2","doi-asserted-by":"publisher","DOI":"10.22331\/q-2022-08-11-774"},{"key":"e_1_3_2_25_2","doi-asserted-by":"publisher","DOI":"10.1613\/jair.1.14329"},{"key":"e_1_3_2_26_2","unstructured":"Natansh Mathur Jonas Landman Yun Yvonna Li Martin Strahm Skander Kazdaghli Anupam Prakash and Iordanis Kerenidis. 2021. 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