A Brief Review of Quantum Machine Learning for Financial Services
<|reference_start|>A Brief Review of Quantum Machine Learning for Financial Services: 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.<|reference_end|>
@article{doosti2024a,
    title={A Brief Review of Quantum Machine Learning for Financial Services},
    author={Mina Doosti, Petros Wallden, Conor Brian Hamill, Robert Hankache,
  Oliver Thomson Brown, and Chris Heunen},
    journal={arXiv preprint arXiv:2407.12618},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.12618},
    primaryClass={quant-ph cs.CE}
}