Financial fraud detection with entropy quantum optimization versus classical machine learning

In this paper, we introduce CVQBoost, a novel classification algorithm that leverages Dirac-3 entropy quantum optimization technology developed by Quantum Computing Inc. We apply CVQBoost to a fraud detection test case and benchmark its performance against XGBoost, a widely used classical ML method. Running on Dirac-3, CVQBoost demonstrates a significant runtime advantage over XGBoost, which we evaluate on high-performance classical hardware comprising up to 48 CPUs and four NVIDIA L4 GPUs using the RAPIDS AI framework. Our results show that CVQBoost maintains competitive accuracy (measured by AUC) while significantly reducing training time, particularly as dataset size and feature complexity increase. To assess scalability, we extend our study to large synthetic datasets ranging from 1M to 70M samples, demonstrating that CVQBoost on Dirac-3 is well-suited for large-scale classification tasks. These findings position CVQBoost as a promising alternative to classical gradient boosting methods, offering superior scalability and efficiency for high-dimensional ML applications such as fraud detection.