A Comparative Analysis of Hybrid Quantum Neural Networks in Binary Credit Defaulting Tasks

Abstract

Quantum machine learning has been shown to offer exponential computational boosts compared to current classical models through the exploitation of quantum mechanical properties such as entanglement and superposition. The finance subfield of fintech, where financial services are constructed around a customer’s demands, is believed to benefit with the use of hybrid quantum-classical machine learning algorithms. Classical algorithms struggle to create accurate, complex, and prompt financial analytical models resulting in over 25% of small and medium sized financial institutions losing customers each year. This study explores whether a hybrid quantum neural network (QNN) design provides a quantum advantage in either accuracy or time in the fintech task of credit defaulting. The hybrid quantum neural network model uses a variational quantum circuit built on a continuous variable architecture. The dataflow of the network consists of a classical network, quantum data encoding (which uses quantum squeezers, interferometers, displacement, and Kerr gates), QNN, and measurement. Although the quantum model took a longer time to train, it reported a higher prediction accuracy of 96.73% compared to 91.20% for the classical network. This work demonstrates the future implications of quantum integration in the financial field and the quantum advantage it offers on classification tasks.