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.