{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,11]],"date-time":"2026-05-11T18:12:19Z","timestamp":1778523139816,"version":"3.51.4"},"reference-count":74,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2026,5,11]],"date-time":"2026-05-11T00:00:00Z","timestamp":1778457600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>Quantum computing is one of the most promising emerging technologies, and quantum machine learning (QML), as one of its key branches, is attracting growing interest for intelligent data processing in education. This study conducted a systematic review of QML in the context of Education 5.0 using the PRISMA 2020 methodology. A total of 48 peer-reviewed articles from Springer, Scopus, IEEE Xplore, PubMed, MDPI, arXiv, and APS were analyzed. The results indicate that QML has significant potential to enhance personalized learning, optimize educational data processing, support curriculum innovation, and foster the development of quantum-related competencies. Representative QML algorithms, including Quantum Support Vector Machines, variational quantum circuits, and quantum neural networks, are identified as key technological enablers for future educational applications. However, significant challenges remain, such as limited access to quantum infrastructure, lack of specialized curricula, hardware constraints, and the need for interdisciplinary training. Overall, this study highlights the growing relevance of QML for adaptive learning, learning analytics, and intelligent educational systems, while emphasizing the need for further empirical validation and scalable implementation in real educational environments.<\/jats:p>","DOI":"10.3390\/a19050379","type":"journal-article","created":{"date-parts":[[2026,5,11]],"date-time":"2026-05-11T17:17:42Z","timestamp":1778519862000},"page":"379","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A Systematic Review of Quantum Machine Learning in Education 5.0: Applications and Future Research Directions"],"prefix":"10.3390","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3737-8694","authenticated-orcid":false,"given":"Jimmy Aurelio","family":"Rosales Huamani","sequence":"first","affiliation":[{"name":"Multidisciplinary Sensing, Universal Accessibility and Machine Learning Group, Facultad de Ingenieria Geologica Minera y Metalurgica, Universidad Nacional de Ingenieria, Lima 15333, Peru"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4708-610X","authenticated-orcid":false,"given":"Jose","family":"Ogosi Auqui","sequence":"additional","affiliation":[{"name":"Facultad de Ingenieria Industrial y de Sistemas, Universidad Nacional Federico Villareal, Lima 15082, Peru"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Pedro","family":"Toribio Pando","sequence":"additional","affiliation":[{"name":"Multidisciplinary Sensing, Universal Accessibility and Machine Learning Group, Facultad de Ingenieria Geologica Minera y Metalurgica, Universidad Nacional de Ingenieria, Lima 15333, Peru"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ernan","family":"Capcha Milla","sequence":"additional","affiliation":[{"name":"Multidisciplinary Sensing, Universal Accessibility and Machine Learning Group, Facultad de Ingenieria Geologica Minera y Metalurgica, Universidad Nacional de Ingenieria, Lima 15333, Peru"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jorge Luis","family":"Quinto Esquivel","sequence":"additional","affiliation":[{"name":"Multidisciplinary Sensing, Universal Accessibility and Machine Learning Group, Facultad de Ingenieria Geologica Minera y Metalurgica, Universidad Nacional de Ingenieria, Lima 15333, Peru"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9131-1618","authenticated-orcid":false,"given":"Jose Luis","family":"Castillo Sequera","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Higher Polytechnic School, Universidad de Alcala, 28805 Madrid, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2026,5,11]]},"reference":[{"key":"ref_1","unstructured":"Siemens, G. 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