Financial Fraud Detection with Entropy Computing

Babak Emami, Wesley Dyk, David Haycraft, Carrie Spear, Lac Nguyen, Nicholas Chancellor

Keywords: Dirac-3, CVQBoost, Classification, Fraud Detection

Abstract:

We introduce CVQBoost, a novel classification algorithm
that leverages early hardware implementing Quantum Computing Inc’s
Entropy Quantum Computing (EQC) paradigm, Dirac-3. We apply CVQ-
Boost to a fraud detection test case and benchmark its performance
against XGBoost, a widely utilized ML method. Running on Dirac-3,
CVQBoost demonstrates a significant runtime advantage over XGBoost,
which we evaluate on high-performance hardware comprising up to 48
CPUs and four NVIDIA L4 GPUs using the RAPIDS AI framework.
Our results show that CVQBoost maintains competitive accuracy (mea-
sured 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 gradient boosting methods, offering superior
scalability and efficiency for high-dimensional ML applications such as
fraud detection.

Submission Number: 24