{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T23:36:22Z","timestamp":1761176182590,"version":"build-2065373602"},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"value":"9781643686318","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,10,21]],"date-time":"2025-10-21T00:00:00Z","timestamp":1761004800000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,10,21]]},"abstract":"<jats:p>Quantum machine learning for spin and molecular systems faces critical challenges of scarce labeled data and computationally expensive simulations. To address these limitations, we introduce Hamiltonian-Masked Autoencoding (HMAE), a novel self-supervised framework that pre-trains transformers on unlabeled quantum Hamiltonians, enabling efficient few-shot transfer learning. Unlike random masking approaches, HMAE employs a physics-informed strategy based on quantum information theory to selectively mask Hamiltonian terms based on their physical significance. Experiments on 12,500 quantum Hamiltonians (60% real-world, 40% synthetic) demonstrate that HMAE achieves 85.3% \u00b1 1.5% accuracy in phase classification and 0.15 \u00b1 0.02 eV MAE in ground state energy prediction with merely 10 labeled examples\u2014a statistically significant improvement (p &lt; 0.01) over classical graph neural networks (78.1% \u00b1 2.1%) and quantum neural networks (76.8% \u00b1 2.3%). Our method\u2019s primary advantage is exceptional sample efficiency\u2014reducing required labeled examples by 3-5\u00d7 compared to baseline methods\u2014though we emphasize that ground truth values for fine-tuning and evaluation still require exact diagonalization or tensor networks. We explicitly acknowledge that our current approach is limited to small quantum systems (specifically limited to 12 qubits during training, with limited extension to 16-20 qubits in testing) and that, while promising within this regime, this size restriction prevents immediate application to larger systems of practical interest in materials science and quantum chemistry.<\/jats:p>","DOI":"10.3233\/faia251031","type":"book-chapter","created":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T09:49:23Z","timestamp":1761126563000},"source":"Crossref","is-referenced-by-count":0,"title":["HMAE: Self-Supervised Few-Shot Learning for Quantum Spin Systems"],"prefix":"10.3233","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1624-9954","authenticated-orcid":false,"given":"Ibne Farabi","family":"Shihab","sequence":"first","affiliation":[{"name":"Department of Computer Science, Iowa State University, Ames, Iowa, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-8276-3878","authenticated-orcid":false,"given":"Sanjeda","family":"Akter","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Iowa State University, Ames, Iowa, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5929-5120","authenticated-orcid":false,"given":"Anuj","family":"Sharma","sequence":"additional","affiliation":[{"name":"Department of Civil, Construction and Environmental Engineering, Iowa State University, Ames, Iowa, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"7437","container-title":["Frontiers in Artificial Intelligence and Applications","ECAI 2025"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/FAIA251031","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T09:49:24Z","timestamp":1761126564000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/FAIA251031"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10,21]]},"ISBN":["9781643686318"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/faia251031","relation":{},"ISSN":["0922-6389","1879-8314"],"issn-type":[{"value":"0922-6389","type":"print"},{"value":"1879-8314","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,10,21]]}}}