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

TL;DR

This paper introduces a quantum convolutional neural network architecture optimized for current hardware, demonstrating high accuracy on real quantum devices and reducing input dimensionality without classical pre-processing.

Contribution

It presents a novel encoding scheme and an automated framework for designing QCNNs, enabling direct processing of standard images on NISQ devices with improved accuracy and efficiency.

Findings

Achieved 96.08% accuracy on MNIST with a 49-qubit QCNN on IBM hardware.

Reduced input dimensionality eliminates classical pre-processing.

Demonstrated advantages in accuracy and convergence speed over classical CNNs.

Abstract

While classical convolutional neural networks (CNNs) have revolutionized image classification, the emergence of quantum computing presents new opportunities for enhancing neural network architectures. Quantum CNNs (QCNNs) leverage quantum mechanical properties and hold potential to outperform classical approaches. However, their implementation on current noisy intermediate-scale quantum (NISQ) devices remains challenging due to hardware limitations. In our research, we address this challenge by introducing an encoding scheme that significantly reduces the input dimensionality. We demonstrate that a primitive QCNN architecture with 49 qubits is sufficient to directly process $28\times 28$ pixel MNIST images, eliminating the need for classical dimensionality reduction pre-processing. Additionally, we propose an automated framework based on expressibility, entanglement, and complexity characteristics to identify the building blocks of QCNNs, parameterized quantum circuits (PQCs). Our approach demonstrates advantages in accuracy and convergence speed with a similar parameter count compared to both hybrid QCNNs and classical CNNs. We validated our experiments on IBM's Heron r2 quantum processor, achieving $96.08\%$ classification accuracy, surpassing the $71.74\%$ benchmark of traditional approaches under identical training conditions. These results represent one of the first implementations of image classifications on real quantum hardware and validate the potential of quantum computing in this area.