Quantum Machine Learning and Its Impact on Finance
Quantum computing is one of the most promising technological advancements of our current generation with a promise to revolutionize many industries including pharma, automobile, finance, and cryptography among others. A recent surge of research activities within this field is to build quantum machine learning algorithms with the ability to run on the near term small scale noisy quantum computers with the prime objective of achieving a quantum advantage i.e. achieving the level of solution that a classical machine learning model would not be able to achieve. In this talk, I will give an overview of the quantum machine learning approaches for generative modeling and supervised learning and see how they impact quantitative finance. Specifically we will talk about building generative quantum models and how to speed up Monte-Carlo sampling.
Dr. Niraj Kumar is a Vice President, Quantum Algorithm Research Lead at JP Morgan Chase&Co. His interests and active research work span the fields of quantum machine learning, verification of quantum devices, and secure quantum communications. He obtained his Ph.D. from Telecom Paristech, Paris where he worked on developing secure quantum communication protocols. He has an avid research interest in quantum for finance and has previously worked with PayPal as the head of Quantum Algorithms, and with PASQAL where he developed quantum algorithms for fraud detection and market forecasting, among other applications.