Quantum Face Recognition With Multigate Quantum Convolutional Neural Network

Abstract

In the last decade, quantum computing has showcased its unique mechanism across diverse fields, highlighting significant potential for data-driven applications requiring substantial computational resources. Within this landscape, quantum machine learning emerges as a promising frontier, poised to harness the unique advantages of quantum computing for machine learning tasks. Nonetheless, the current generation of quantum hardware, typified by noisy intermediate-scale quantum (NISQ) devices, grapples with severe resource constraints, particularly in terms of qubit availability. While quantum computing offers tantalizing capabilities such as superposition and entanglement, which can be strategically leveraged to optimize the performance of quantum neural networks, the challenge remains in mitigating the resource limitations while upholding high recognition accuracy. To address this imperative, we introduce a pioneering face recognition method christened the multigate quantum convolutional neural network (MG-QCNN). This innovation is engineered to surmount the resource bottleneck endemic to NISQ devices while preserving exceptional recognition accuracy. Our empirical investigations conducted on benchmark datasets, including the Yale face dataset and the ORL face database, illuminate the remarkable potential of this approach. Specifically, our proposed variational quantum circuit architecture consistently achieves an impressive average accuracy of 96%, which is better than the 95% of the classic CNN. Our model underscores the efficacy of quantum convolution operations in the extraction of feature maps, exhibiting a transformative stride toward unlocking the full potential of quantum-enhanced face recognition, and compared with other quantum models, our method has more advantages in accuracy and efficiency. harnessed the unique capabilities of quantum computing to overcome resource limitations and achieve an astounding 96% average accuracy on face recognition tasks. This achievement not only showcases the immediate potential of quantum convolution operations in feature extraction but also sets the stage for a quantum revolution in the field of machine learning. Our work is a catalyst for future explorations, promising even greater computational efficiency and accuracy as we scale up quantum structures and expand our horizons to high-resolution color face images. This study is a foundational step toward quantum-enhanced face recognition, with far-reaching implications for data-driven applications and the broader field of artificial intelligence.