Improved Financial Forecasting via Quantum Machine Learning

arXiv:2306.12965v2 Announce Type: replace-cross
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 …

abstract algorithms applications arxiv churn cs.lg domains financial financial forecasting forecasting machine machine learning processes q-fin.st quant-ph quantum random show type via work