Improved Financial Forecasting via Quantum Machine Learning

TL;DR

This paper explores how quantum machine learning techniques can improve financial forecasting by enhancing classical models and designing efficient quantum neural networks, showing promising results in churn prediction and credit risk assessment.

Contribution

It introduces quantum-enhanced methods for financial prediction, demonstrating improved accuracy and efficiency over classical approaches, and paves the way for future quantum hardware applications.

Findings

Quantum DPPs improve churn prediction precision by 6%

Quantum neural networks match classical performance with fewer parameters

Quantum-inspired classical solutions show potential for future advancements

Abstract

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.