A Time-Nonlocal Optimization Approach for Image classification

Abstract

Image classification remains a fundamental problem in artificial intelligence, with applications spanning medical diagnostics, autonomous systems, and security surveillance. Traditional deep learning models, such as Convolutional Neural Networks (CNNs), have achieved state-of-the-art performance in image classification tasks. However, these models suffer from high computational costs and limitations in optimization efficiency. Recently, quantum computing has emerged as a promising alternative, particularly in hybrid quantum-classical models that leverage quantum computational advantages for improved learning. This study introduces a novel Optimization-Based Image Classification Framework using a Time-Nonlocal Optimization Approach. Our method integrates time-dependent parameterization to smooth gradient variations, thereby mitigating the barren plateau problem commonly observed in quantum machine learning. By leveraging time-nonlocal optimization, we enhance the training stability and convergence of quantum variational models, making them more viable for high-dimensional classification tasks. We evaluate our proposed framework on benchmark datasets, including Iris and MNIST, to demonstrate its effectiveness. Experimental results show that our approach significantly improves classification accuracy while reducing training inefficiencies compared to standard quantum optimization techniques. The proposed method also offers improved scalability, making it a practical solution for hybrid quantum-classical image classification. This research contributes to the advancement of quantum-enhanced image classification by addressing key optimization challenges and demonstrating the feasibility of time-nonlocal optimization in quantum-classical hybrid learning. The findings of this work provide a foundation for future developments in quantum-assisted deep learning and optimization-based image processing.