Selective Feature Re-Encoded Quantum Convolutional Neural Network with Joint Optimization for Image Classification

TL;DR

This paper introduces a novel quantum convolutional neural network architecture with selective feature re-encoding and joint optimization, significantly improving image classification accuracy on MNIST datasets by leveraging quantum principles and classical feature extraction methods.

Contribution

It proposes a new QCNN architecture with selective feature re-encoding and parallel integration of PCA and Autoencoders, enhancing classification performance over existing models.

Findings

Selective feature re-encoding improves quantum feature processing.

Joint optimization enhances model accuracy and generalization.

Parallel QCNN outperforms individual QCNNs and ensemble methods.

Abstract

Quantum Machine Learning (QML) has seen significant advancements, driven by recent improvements in Noisy Intermediate-Scale Quantum (NISQ) devices. Leveraging quantum principles such as entanglement and superposition, quantum convolutional neural networks (QCNNs) have demonstrated promising results in classifying both quantum and classical data. This study examines QCNNs in the context of image classification and proposes a novel strategy to enhance feature processing and a QCNN architecture for improved classification accuracy. First, a selective feature re-encoding strategy is proposed, which directs the quantum circuits to prioritize the most informative features, thereby effectively navigating the crucial regions of the Hilbert space to find the optimal solution space. Secondly, a novel parallel-mode QCNN architecture is designed to simultaneously incorporate features extracted by two classical methods, Principal Component Analysis (PCA) and Autoencoders, within a unified training scheme. The joint optimization involved in the training process allows the QCNN to benefit from complementary feature representations, enabling better mutual readjustment of model parameters. To assess these methodologies, comprehensive experiments have been performed using the widely used MNIST and Fashion MNIST datasets for binary classification tasks. Experimental findings reveal that the selective feature re-encoding method significantly improves the quantum circuit's feature processing capability and performance. Furthermore, the jointly optimized parallel QCNN architecture consistently outperforms the individual QCNN models and the traditional ensemble approach involving independent learning followed by decision fusion, confirming its superior accuracy and generalization capabilities.