Quantum machine learning for time series prediction

Abstract

Time series prediction is an essential task in various fields, such as meteorology, finance and healthcare. Traditional approaches to time series prediction have primarily relied on regression and moving average methods, but recent advancements have seen a growing interest in applying machine learning techniques. With the rise of quantum computing, it is of interest to explore whether quantum machine learning can offer advantages over classical methods for time series forecasting. This thesis presents the first large-scale systematic benchmark comparing classical and quantum models for time series prediction. A variety of quantum models are evaluated against classical counterparts on different datasets. A novel quantum reservoir computing architecture is proposed, demonstrating promising results in handling nonlinear prediction tasks. The findings suggest that, for simpler time series prediction tasks, quantum models achieve accuracy comparable to classical methods. However, for more complex tasks, such as long-term forecasting, certain quantum models show improved performance. While current quantum machine learning models do not consistently outperform classical approaches, the results point to specific contexts where quantum methods may be beneficial.