A Time-Nonlocal Optimization Approach for Image classification

Abstract

Image classification remains a fundamental problem in artificial intelligence, with applicationsspanning medical diagnostics, autonomous systems, and security surveillance.Traditional deep learning models, such as Convolutional Neural Networks (CNNs), haveachieved state-of-the-art performance in image classification tasks. However, these modelssuffer from high computational costs and limitations in optimization efficiency. Recently,quantum computing has emerged as a promising alternative, particularly in hybridquantum-classical models that leverage quantum computational advantages for improvedlearning. This study introduces a novel Optimization-Based Image ClassificationFramework using a Time-Nonlocal Optimization Approach. Our method integratestime-dependent parameterization to smooth gradient variations, thereby mitigatingthe barren plateau problem commonly observed in quantum machine learning. By leveragingtime-nonlocal optimization, we enhance the training stability and convergence ofquantum variational models, making them more viable for high-dimensional classificationtasks. We evaluate our proposed framework on benchmark datasets, including Iris andMNIST, to demonstrate its effectiveness. Experimental results show that our approachsignificantly improves classification accuracy while reducing training inefficiencies comparedto standard quantum optimization techniques. The proposed method also offersimproved scalability, making it a practical solution for hybrid quantum-classical imageclassification. This research contributes to the advancement of quantum-enhanced imageclassification by addressing key optimization challenges and demonstrating the feasibilityof time-nonlocal optimization in quantum-classical hybrid learning. The findings of thiswork provide a foundation for future developments in quantum-assisted deep learning andoptimization-based image processing.