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While classical convolutional neural networks (CNNs) have revolutionizedimage classification, the emergence of quantum computing presents newopportunities for enhancing neural network architectures. Quantum CNNs (QCNNs)leverage quantum mechanical properties and hold potential to outperformclassical approaches. However, their implementation on current noisyintermediate-scale quantum (NISQ) devices remains challenging due to hardwarelimitations. In our research, we address this challenge by introducing anencoding scheme that significantly reduces the input dimensionality. Wedemonstrate that a primitive QCNN architecture with 49 qubits is sufficient todirectly process $28\times 28$ pixel MNIST images, eliminating the need forclassical dimensionality reduction pre-processing. Additionally, we propose anautomated framework based on expressibility, entanglement, and complexitycharacteristics to identify the building blocks of QCNNs, parameterized quantumcircuits (PQCs). Our approach demonstrates advantages in accuracy andconvergence speed with a similar parameter count compared to both hybrid QCNNsand classical CNNs. We validated our experiments on IBM's Heron r2 quantumprocessor, achieving $96.08\%$ classification accuracy, surpassing the$71.74\%$ benchmark of traditional approaches under identical trainingconditions. These results represent one of the first implementations of imageclassifications on real quantum hardware and validate the potential of quantumcomputing in this area.

Efficient Quantum Convolutional Neural Networks for Image Classification: Overcoming Hardware Constraints