{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,9]],"date-time":"2026-05-09T08:17:56Z","timestamp":1778314676415,"version":"3.51.4"},"reference-count":71,"publisher":"IOP Publishing","issue":"2","license":[{"start":{"date-parts":[[2024,5,22]],"date-time":"2024-05-22T00:00:00Z","timestamp":1716336000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2024,5,22]],"date-time":"2024-05-22T00:00:00Z","timestamp":1716336000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/iopscience.iop.org\/info\/page\/text-and-data-mining"}],"content-domain":{"domain":["iopscience.iop.org"],"crossmark-restriction":false},"short-container-title":["Mach. Learn.: Sci. 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We present a hybrid quantum PINN (HQPINN) that simulates laminar fluid flow in 3D\n                    <jats:italic>Y<\/jats:italic>\n                    -shaped mixers. Our approach combines the expressive power of a quantum model with the flexibility of a PINN, resulting in a 21% higher accuracy compared to a purely classical neural network. Our findings highlight the potential of machine learning approaches, and in particular HQPINN, for complex shape optimization tasks in computational fluid dynamics. By improving the accuracy of fluid simulations in complex geometries, our research using hybrid quantum models contributes to the development of more efficient and reliable fluid dynamics solvers.\n                  <\/jats:p>","DOI":"10.1088\/2632-2153\/ad43b2","type":"journal-article","created":{"date-parts":[[2024,4,25]],"date-time":"2024-04-25T18:43:22Z","timestamp":1714070602000},"page":"025045","update-policy":"https:\/\/doi.org\/10.1088\/crossmark-policy","source":"Crossref","is-referenced-by-count":28,"title":["Hybrid quantum physics-informed neural networks for simulating computational fluid dynamics in complex shapes"],"prefix":"10.1088","volume":"5","author":[{"ORCID":"https:\/\/orcid.org\/0009-0003-8431-0873","authenticated-orcid":false,"given":"Alexandr","family":"Sedykh","sequence":"first","affiliation":[]},{"given":"Maninadh","family":"Podapaka","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0009-0009-3931-3702","authenticated-orcid":false,"given":"Asel","family":"Sagingalieva","sequence":"additional","affiliation":[]},{"given":"Karan","family":"Pinto","sequence":"additional","affiliation":[]},{"given":"Markus","family":"Pflitsch","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5033-4063","authenticated-orcid":true,"given":"Alexey","family":"Melnikov","sequence":"additional","affiliation":[]}],"member":"266","published-online":{"date-parts":[[2024,5,22]]},"reference":[{"key":"mlstad43b2bib1","doi-asserted-by":"publisher","DOI":"10.1063\/1.5066893","article-title":"A review: fundamentals of computational fluid dynamics (CFD)","volume":"2030","author":"Zawawi","year":"2018","journal-title":"AIP Conf. 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