Revolutionizing Financial Services with Quantum Machine Learning Techniques

Abstract

Modern quantum machine learning algorithms and techniques are reviewed in this review paper with possible financial applications. Along with quantum generative AI techniques like Quantum Transformers and Quantum Graph Neural Networks (QGNNs), we discuss QML techniques in supervised learning tasks like Quantum Variational Classifiers, Quantum Kernel Estimation, and Quantum Neural Networks (QNNs). Risk management, credit scoring, fraud detection, and stock price prediction are among the financial applications that are taken into consideration. Additionally, we offer a summary of QML's drawbacks, possibilities, and restrictions in these particular domains as well as more widely throughout the field. With the help of this, we hope to provide data scientists, financial industry professionals, and enthusiasts with a quick overview of why quantum computing, and QML in particular, might be worthwhile to investigate in their respective fields of expertise.