{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:32:03Z","timestamp":1760146323630,"version":"build-2065373602"},"reference-count":87,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2024,10,24]],"date-time":"2024-10-24T00:00:00Z","timestamp":1729728000000},"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>We introduce the concept-driven quantum neural network (CD-QNN), an innovative architecture designed to enhance the interpretability of quantum neural networks (QNNs). CD-QNN merges the representational capabilities of QNNs with the transparency of self-explanatory models by mapping input data into a human-understandable concept space and making decisions based on these concepts. The algorithmic design of CD-QNN is comprehensively analyzed, detailing the roles of the concept generator, feature extractor, and feature integrator in improving and balancing model expressivity and interpretability. Experimental results demonstrate that CD-QNN maintains high predictive accuracy while offering clear and meaningful explanations of its decision-making process. This paradigm shift in QNN design underscores the growing importance of interpretability in quantum artificial intelligence, positioning CD-QNN and its derivative technologies as pivotal in advancing reliable and interpretable quantum intelligent systems for future research and applications.<\/jats:p>","DOI":"10.3390\/e26110902","type":"journal-article","created":{"date-parts":[[2024,10,25]],"date-time":"2024-10-25T08:42:13Z","timestamp":1729845733000},"page":"902","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Mapping Data to Concepts: Enhancing Quantum Neural Network Transparency with Concept-Driven Quantum Neural Networks"],"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"}]},{"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"}]}],"member":"1968","published-online":{"date-parts":[[2024,10,24]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1484","DOI":"10.1137\/S0097539795293172","article-title":"Polynomial-Time Algorithms for Prime Factorization and Discrete Logarithms on a Quantum Computer","volume":"26","author":"Shor","year":"1997","journal-title":"SIAM J. 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