Quantum Machine Learning for Financial Services

Expanding Frontiers: An Alternative Investments & Machine Learning Podcast

This is the second Expanding Frontiers episode devoted to quantum computing for finance. It explores the current (2024) state and future potential of Quantum Machine Learning (QML), specifically focusing on its applications within the financial services industry. We discuss various QML algorithms including Quantum Variational Classifiers, Quantum Kernel Estimation, and Quantum Neural Networks, and also touch upon quantum generative AI techniques like Quantum Transformers and Quantum Graph Neural Networks. The paper discussed identifies key financial applications for QML, such as risk management, credit scoring, fraud detection, and stock price prediction, while also outlining the promises and limitations of integrating QML into real-world financial operations. The review aims to serve as a practical guide for financial professionals and data scientists interested in understanding QML's relevance to their field.

References

A Brief Review of Quantum Machine Learning for Financial Services (July 2024)

Mina Doosti, Petros Wallden, Conor Brian Hamill, Robert Hankache, Oliver Thomson Brown, Chris Heunen
https://doi.org/10.48550/arXiv.2407.12618

Resources

Medium article: Are You Ready to Learn About Quantum Computing?

Podcast Disclaimer

This podcast is an independent production and is not affiliated with or endorsed by any third-party entities unless explicitly stated. The content is for educational and informational purposes only and does not constitute financial, investment, legal, or professional advice. Listeners should consult qualified professionals before making any decisions based on this content.

This episode is based on the references listed above and was generated using Notebook LM and other AI tools. While I have reviewed the content for accuracy, it may still contain errors, inaccuracies, or omissions. Neither the producers nor any affiliates accept liability for any damages or losses arising from the use or interpretation of this content.