{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,25]],"date-time":"2026-04-25T15:03:32Z","timestamp":1777129412901,"version":"3.51.4"},"reference-count":35,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2025,8,1]],"date-time":"2025-08-01T00:00:00Z","timestamp":1754006400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Committee of Science of the Ministry of Science and Higher Education of the Republic of Kazakhstan","award":["AP19576314"],"award-info":[{"award-number":["AP19576314"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Computation"],"abstract":"<jats:p>Quantum machine learning (QML) has emerged as a promising approach for enhancing image classification by exploiting quantum computational principles such as superposition and entanglement. However, practical applications on complex datasets like CIFAR-100 remain limited due to the low expressivity of shallow circuits and challenges in circuit optimization. In this study, we propose HQCNN\u2013REGA\u2014a novel hybrid quantum\u2013classical convolutional neural network architecture that integrates data re-uploading and genetic algorithm optimization for improved performance. The data re-uploading mechanism allows classical inputs to be encoded multiple times into quantum states, enhancing the model\u2019s capacity to learn complex visual features. In parallel, a genetic algorithm is employed to evolve the quantum circuit architecture by optimizing gate sequences, entanglement patterns, and layer configurations. This combination enables automatic discovery of efficient parameterized quantum circuits without manual tuning. Experiments on the MNIST and CIFAR-100 datasets demonstrate state-of-the-art performance for quantum models, with HQCNN\u2013REGA outperforming existing quantum neural networks and approaching the accuracy of advanced classical architectures. In particular, we compare our model with classical convolutional baselines such as ResNet-18 to validate its effectiveness in real-world image classification tasks. Our results demonstrate the feasibility of scalable, high-performing quantum\u2013classical systems and offer a viable path toward practical deployment of QML in computer vision applications, especially on noisy intermediate-scale quantum (NISQ) hardware.<\/jats:p>","DOI":"10.3390\/computation13080185","type":"journal-article","created":{"date-parts":[[2025,8,4]],"date-time":"2025-08-04T10:48:08Z","timestamp":1754304488000},"page":"185","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["A Hybrid Quantum\u2013Classical Architecture with Data Re-Uploading and Genetic Algorithm Optimization for Enhanced Image Classification"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4699-0436","authenticated-orcid":false,"given":"Aksultan","family":"Mukhanbet","sequence":"first","affiliation":[{"name":"LLP \u201cDigitAlem\u201d, Almaty 050042, Kazakhstan"},{"name":"Faculty of Information Technology, Al-Farabi Kazakh National University, Almaty 050040, Kazakhstan"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1313-9004","authenticated-orcid":false,"given":"Beimbet","family":"Daribayev","sequence":"additional","affiliation":[{"name":"LLP \u201cDigitAlem\u201d, Almaty 050042, Kazakhstan"},{"name":"Graduate School of Digital Technologies and Construction, Shakarim University, Semey 071412, Kazakhstan"}]}],"member":"1968","published-online":{"date-parts":[[2025,8,1]]},"reference":[{"key":"ref_1","unstructured":"Nielsen, M.A., and Chuang, I.L. 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