Mr Jack Waller

Research project: The Application of Quantum Machine Learning Models to Evaluate Financial Markets

Abstract

In the realm of high-frequency trading even the slightest improvement in speed and accuracy can translate to a significant competitive advantage. Quantum machine learning models, which integrate quantum algorithms into existing machine learning models, hold enormous potential in this field. Through the inclusion of quantum algorithms within machine learning structures, an advancement in training times, time to convergence and enhanced precision in comparison to classical models can be produced. The increased potential capabilities of the quantum machine learning models could be the key to gaining a substantial competitive edge in the financial markets.

My research looks to explore the transformative potential of quantum machine learning in the financial markets and other prospective fields. I aim to construct, enhance, and refine proposed quantum machine learning methodologies and employ these models in financial applications where their classical machine learning counterparts are typically used. This process will allow for the evaluation of speed, accuracy, and computational efficiency, contributing insights into the advantages and limitations of quantum machine learning approaches.