  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.
