{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,13]],"date-time":"2026-06-13T03:24:00Z","timestamp":1781321040137,"version":"3.54.1"},"reference-count":31,"publisher":"Verein zur Forderung des Open Access Publizierens in den Quantenwissenschaften","license":[{"start":{"date-parts":[[2025,2,20]],"date-time":"2025-02-20T00:00:00Z","timestamp":1740009600000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Severo Ochoa","award":["CEX2019-000910-S"],"award-info":[{"award-number":["CEX2019-000910-S"]}]},{"name":"FUNQIP and European Union NextGenerationEU","award":["PRTR-C17.I1"],"award-info":[{"award-number":["PRTR-C17.I1"]}]},{"DOI":"10.13039\/501100000780","name":"European Union","doi-asserted-by":"crossref","award":["PASQuanS2.1, 101113690"],"award-info":[{"award-number":["PASQuanS2.1, 101113690"]}],"id":[{"id":"10.13039\/501100000780","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["quantum-journal.org"],"crossmark-restriction":false},"short-container-title":["Quantum"],"abstract":"<jats:p>Quantum machine learning is arguably one of the most explored applications of near-term quantum devices. Much focus has been put on notions of variational quantum machine learning where <mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\"><mml:mrow class=\"MJX-TeXAtom-ORD\"><mml:mtext class=\"MJX-tex-mathit\" mathvariant=\"italic\">parameterized quantum circuits<\/mml:mtext><\/mml:mrow><\/mml:math> (PQCs) are used as learning models. These PQC models have a rich structure which suggests that they might be amenable to efficient dequantization via <mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\"><mml:mrow class=\"MJX-TeXAtom-ORD\"><mml:mtext class=\"MJX-tex-mathit\" mathvariant=\"italic\">random Fourier features<\/mml:mtext><\/mml:mrow><\/mml:math> (RFF). In this work, we establish necessary and sufficient conditions under which RFF does indeed provide an efficient dequantization of variational quantum machine learning for regression. We build on these insights to make concrete suggestions for PQC architecture design, and to identify structures which are necessary for a regression problem to admit a potential quantum advantage via PQC based optimization.<\/jats:p>","DOI":"10.22331\/q-2025-02-20-1640","type":"journal-article","created":{"date-parts":[[2025,2,20]],"date-time":"2025-02-20T14:39:01Z","timestamp":1740062341000},"page":"1640","update-policy":"https:\/\/doi.org\/10.22331\/q-crossmark-policy-page","source":"Crossref","is-referenced-by-count":16,"title":["Potential and limitations of random Fourier features for dequantizing quantum machine learning"],"prefix":"10.22331","volume":"9","author":[{"given":"Ryan","family":"Sweke","sequence":"first","affiliation":[{"name":"IBM Quantum, Almaden Research Center, San Jose, CA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Erik","family":"Recio-Armengol","sequence":"additional","affiliation":[{"name":"ICFO-Institut de Ciencies Fotoniques, The Barcelona Institute of Science and Technology, 08860 Castelldefels, Spain"},{"name":"Eurecat, Centre Tecnologic de Catalunya, Multimedia Technologies, Barcelona, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sofiene","family":"Jerbi","sequence":"additional","affiliation":[{"name":"Dahlem Center for Complex Quantum Systems, Freie Universit\u00e4t Berlin, Berlin, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Elies","family":"Gil-Fuster","sequence":"additional","affiliation":[{"name":"Dahlem Center for Complex Quantum Systems, Freie Universit\u00e4t Berlin, Berlin, Germany"},{"name":"Fraunhofer Heinrich Hertz Institute, 10587 Berlin, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bryce","family":"Fuller","sequence":"additional","affiliation":[{"name":"IBM Quantum, IBM T.J. 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