A Brief Review of Quantum Machine Learning for Financial Services

TL;DR

This paper surveys the promise and limits of quantum machine learning (QML) in finance, focusing on QML techniques that operate on classical data. It categorizes approaches into supervised learning with quantum-enhanced feature spaces (including Quantum Variational Classifier and Quantum Kernel Estimation), quantum neural networks, and generative/graph-based quantum methods, with emphasis on credit scoring, risk management, fraud detection, and stock forecasting. It highlights near-term opportunities on Noisy Intermediate-Scale Quantum (NISQ) devices (QVC, QKE) and longer-term potential from Quantum Neural Networks, quantum transformers, and Quantum Graph Neural Networks, while candidly addressing data-upload, training, and hardware challenges. The article also covers QGenAI avenues like QCBM, QBM, and QGAN, and the role of quantum transformers, detailing architectures, complexities, and early empirical results, to provide a pragmatic, hype-free guide for finance professionals and data scientists.

Abstract

This review paper examines state-of-the-art algorithms and techniques in quantum machine learning with potential applications in finance. We discuss QML techniques in supervised learning tasks, such as Quantum Variational Classifiers, Quantum Kernel Estimation, and Quantum Neural Networks (QNNs), along with quantum generative AI techniques like Quantum Transformers and Quantum Graph Neural Networks (QGNNs). The financial applications considered include risk management, credit scoring, fraud detection, and stock price prediction. We also provide an overview of the challenges, potential, and limitations of QML, both in these specific areas and more broadly across the field. We hope that this can serve as a quick guide for data scientists, professionals in the financial sector, and enthusiasts in this area to understand why quantum computing and QML in particular could be interesting to explore in their field of expertise.