A Hybrid Quantum–Classical Architecture with Data Re-Uploading and Genetic Algorithm Optimization for Enhanced Image Classification

Mukhanbet A. Daribayev B.
August 2025 Multidisciplinary Digital Publishing Institute (MDPI)

Computation
2025 #13 Issue 8

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–REGA—a novel hybrid quantum–classical 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’s 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–REGA 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–classical systems and offer a viable path toward practical deployment of QML in computer vision applications, especially on noisy intermediate-scale quantum (NISQ) hardware.

circuit optimization , data re-uploading , genetic algorithm , hybrid quantum–classical models , image classification , quantum convolutional neural network , quantum machine learning

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LLP “DigitAlem”, Almaty, 050042, Kazakhstan
Faculty of Information Technology, Al-Farabi Kazakh National University, Almaty, 050040, Kazakhstan
Graduate School of Digital Technologies and Construction, Shakarim University, Semey, 071412, Kazakhstan

LLP “DigitAlem”
Faculty of Information Technology
Graduate School of Digital Technologies and Construction

10 лет помогаем публиковать статьи Международный издатель

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