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

• URL: http://arxiv.org/abs/2509.13818v1
• Date: Wed, 17 Sep 2025 08:36:05 GMT
• Title: Hybrid Quantum-Classical Neural Networks for Few-Shot Credit Risk Assessment
• Authors: Zheng-an Wang, Yanbo J. Wang, Jiachi Zhang, Qi Xu, Yilun Zhao, Jintao Li, Yipeng Zhang, Bo Yang, Xinkai Gao, Xiaofeng Cao, Kai Xu, Pengpeng Hao, Xuan Yang, Heng Fan,
• Abstract summary: This work tackles the challenge of few-shot credit risk assessment.<n>We design and implement a novel hybrid quantum-classical workflow.<n>A Quantum Neural Network (QNN) was trained via the parameter-shift rule.<n>On a real-world credit dataset of 279 samples, our QNN achieved a robust average AUC of 0.852 +/- 0.027 in simulations and yielded an impressive AUC of 0.88 in the hardware experiment.
• Score: 52.05742536403784
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Quantum Machine Learning (QML) offers a new paradigm for addressing complex financial problems intractable for classical methods. This work specifically tackles the challenge of few-shot credit risk assessment, a critical issue in inclusive finance where data scarcity and imbalance limit the effectiveness of conventional models. To address this, we design and implement a novel hybrid quantum-classical workflow. The methodology first employs an ensemble of classical machine learning models (Logistic Regression, Random Forest, XGBoost) for intelligent feature engineering and dimensionality reduction. Subsequently, a Quantum Neural Network (QNN), trained via the parameter-shift rule, serves as the core classifier. This framework was evaluated through numerical simulations and deployed on the Quafu Quantum 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 yielded an impressive AUC of 0.88 in the hardware experiment. This performance surpasses a suite of classical benchmarks, with a particularly strong result on the recall metric. This study provides a pragmatic blueprint for applying quantum computing to data-constrained financial scenarios in the NISQ era and offers valuable empirical evidence supporting its potential in high-stakes applications like inclusive finance.

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