  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.
