{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:35:50Z","timestamp":1760146550631,"version":"build-2065373602"},"reference-count":23,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2024,11,17]],"date-time":"2024-11-17T00:00:00Z","timestamp":1731801600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China","award":["62372459","62376282","91948303"],"award-info":[{"award-number":["62372459","62376282","91948303"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>Quantum generative models have shown promise in fields such as quantum chemistry, materials science, and optimization. However, their practical utility is hindered by a significant challenge: the lack of interpretability. In this work, we introduce model inversion to enhance both the interpretability and controllability of quantum generative models. Model inversion allows for tracing generated quantum states back to their latent variables, revealing the relationship between input parameters and generated outputs. We apply this method to models generating ground states for Hamiltonians, such as the transverse-field Ising model (TFIM) and generalized cluster Hamiltonians, achieving interpretability control without retraining the model. Experimental results demonstrate that our approach can accurately guide the generated quantum states across different quantum phases. This framework bridges the gap between theoretical models and practical applications by providing transparency and fine-tuning capabilities, particularly in high-stakes environments like drug discovery and material design.<\/jats:p>","DOI":"10.3390\/e26110987","type":"journal-article","created":{"date-parts":[[2024,11,20]],"date-time":"2024-11-20T09:14:38Z","timestamp":1732094078000},"page":"987","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Toward Transparent and Controllable Quantum Generative Models"],"prefix":"10.3390","volume":"26","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7147-712X","authenticated-orcid":false,"given":"Jinkai","family":"Tian","sequence":"first","affiliation":[{"name":"Intelligent Game and Decision Lab, Beijing 100071, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wenjing","family":"Yang","sequence":"additional","affiliation":[{"name":"Department of Intelligent Data Science, College of Computer Science and Technology, National University of Defense Technology, Changsha 410073, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2024,11,17]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"12321","DOI":"10.1109\/TPAMI.2023.3272029","article-title":"Recent Advances for Quantum Neural Networks in Generative Learning","volume":"45","author":"Tian","year":"2023","journal-title":"IEEE Trans. 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