Improved Financial Forecasting via Quantum Machine Learning

Understand

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.