Improved Financial Forecasting via Quantum Machine Learning

Quantum computing is a rapidly evolving field that promises to revolutionize various domains, and finance is no exception. There is a variety of computationally hard financial problems for which quantum algorithms can potentially offer advantages [24, 16, 39, 6], for example in combinatorial optimization [34, 42], convex optimization [30, 43], monte carlo simulations [15, 44, 21], and machine learning [41, 18, 1]. In this work, we explore the potential of quantum machine learning methods in improving the performance of forecasting in finance, specifically focusing on two use cases within the business of Itaú Unibanco, the largest bank in Latin America. In the first use case, we aim to improve the performance of Random Forest methods for churn prediction. We introduce quantum algorithms for Determinantal Point Processes (DPP) sampling [29], and develop a method of DPP sampling to enhance Random Forest models.