Leveraging Quantum Machine Learning for Optimization Problems in Complex Systems

Abstract

A revolutionary method that has great promise for resolving optimization issues in complicated systems is quantum machine learning, or QML. Large-scale optimization in logistics, finance, and material design are just a few examples of the problems that can be solved by QML algorithms by utilizing the concepts of quantum computing. combining machine learning methods with quantum computing to increase the precision and efficiency of optimization solutions. We examine current developments in variational quantum circuits, quantum annealing, and hybrid quantum-classical algorithms with an emphasis on how they are applied to actual optimization issues. We also talk about the difficulties posed by the limitations of present quantum gear and the methods being developed to get around them. We demonstrate how QML can solve extremely complicated optimization tasks with exponential speedups by utilizing quantum algorithms. This makes it a viable area for future developments in domains that demand complex resource allocation and decision-making.