{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,2]],"date-time":"2026-06-02T18:23:05Z","timestamp":1780424585227,"version":"3.54.1"},"reference-count":43,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2020,7,29]],"date-time":"2020-07-29T00:00:00Z","timestamp":1595980800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"U.S. Department of Energy (Office of Basic Energy Sciences)","award":["DE-SC0019215"],"award-info":[{"award-number":["DE-SC0019215"]}]},{"DOI":"10.13039\/100006377","name":"Purdue University","doi-asserted-by":"publisher","award":["IDSI F.90000303"],"award-info":[{"award-number":["IDSI F.90000303"]}],"id":[{"id":"10.13039\/100006377","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>We present a hybrid quantum-classical neural network that can be trained to perform electronic structure calculation and generate potential energy curves of simple molecules. The method is based on the combination of parameterized quantum circuits and measurements. With unsupervised training, the neural network can generate electronic potential energy curves based on training at certain bond lengths. To demonstrate the power of the proposed new method, we present the results of using the quantum-classical hybrid neural network to calculate ground state potential energy curves of simple molecules such as H2, LiH, and BeH2. The results are very accurate and the approach could potentially be used to generate complex molecular potential energy surfaces.<\/jats:p>","DOI":"10.3390\/e22080828","type":"journal-article","created":{"date-parts":[[2020,7,29]],"date-time":"2020-07-29T07:31:45Z","timestamp":1596007905000},"page":"828","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":38,"title":["Hybrid Quantum-Classical Neural Network for Calculating Ground State Energies of Molecules"],"prefix":"10.3390","volume":"22","author":[{"given":"Rongxin","family":"Xia","sequence":"first","affiliation":[{"name":"Department of Chemistry, Purdue University, West Lafayette, IN 47906, USA"},{"name":"Department of Physics and Astronomy, Purdue University, West Lafayette, IN 47906, USA"},{"name":"Birck Nanotechnology Center, Purdue University, West Lafayette, IN 47906, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0574-5346","authenticated-orcid":false,"given":"Sabre","family":"Kais","sequence":"additional","affiliation":[{"name":"Department of Chemistry, Purdue University, West Lafayette, IN 47906, USA"},{"name":"Department of Physics and Astronomy, Purdue University, West Lafayette, IN 47906, USA"},{"name":"Birck Nanotechnology Center, Purdue University, West Lafayette, IN 47906, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2020,7,29]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Kais, S. 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