Improved Financial Forecasting via Quantum Machine Learning
This work addresses financial forecasting for businesses, but it is incremental as it builds on existing quantum-inspired classical methods.
The paper tackled financial forecasting by applying quantum machine learning to churn prediction and credit risk assessment, achieving a 6% precision improvement in churn prediction and matching classical performance with fewer parameters in credit risk.
Quantum algorithms have the potential to enhance machine learning across a variety of domains and applications. In this work, we show how quantum machine learning can be used to improve financial forecasting. First, we use classical and quantum Determinantal Point Processes to enhance Random Forest models for churn prediction, improving precision by almost 6%. Second, we design quantum neural network architectures with orthogonal and compound layers for credit risk assessment, which match classical performance with significantly fewer parameters. Our results demonstrate that leveraging quantum ideas can effectively enhance the performance of machine learning, both today as quantum-inspired classical ML solutions, and even more in the future, with the advent of better quantum hardware.