arXiv:2509.13818 · Chinese Physics B

Hybrid Quantum-Classical Neural Networks for Few-Shot Credit Risk Assessment

Zhengan Wang, Yanbo J. Wang, Jiachi Zhang, Qi Xu, Yilun Zhao, Jintao Li, Yipeng Zhang, Bo Yang, X. Gao, Xiaofeng Cao, Kai Xu, Pengpeng Hao, Xuan Yang, Heng Fan

Published Nov 19, 2025• 0 citations

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Abstract

Abstract Quantum Machine Learning (QML) presents a novel paradigm for financial modeling, particularly for the challenge of few-shot credit risk assessment, which is a critical issue in inclusive finance where data scarcity limits conventional models. To address this, we propose and implement a hybrid quantum-classical workflow. Our methodology first utilizes a classical model ensemble for dimensionality reduction, creating a dense, three-dimensional feature representation from the original data. This representation then serves as input to a Quantum Neural Network (QNN), trained with the parameter-shift rule, which acts as the core classifier. The framework’s efficacy was validated through numerical simulations and on real hardware using the Quafu Cloud Platform’s ScQ-P21 superconducting processor. On a real-world credit dataset of 279 samples, our QNN achieved a robust average AUC of 0.852 ± 0.027 in simulations and an impressive AUC of 0.89 in the hardware experiment. This performance surpasses a suite of classical benchmarks, with a particularly strong result on the recall metric—crucial for minimizing false negatives in risk control. This study provides a pragmatic blueprint for applying QML to data-constrained financial scenarios and offers valuable empirical evidence of its potential in the NISQ era.

DOI: 10.1088/1674-1056/ae2115

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