{"ID":2869768,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.13818","arxiv_id":"2509.13818","title":"Hybrid Quantum-Classical Neural Networks for Few-Shot Credit Risk Assessment","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.","short_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 convention...","url_abs":"https://arxiv.org/abs/2509.13818","url_pdf":"https://arxiv.org/pdf/2509.13818v1","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\"]","published":"2025-09-17T08:36:05Z","proceeding":"cs.LG","tasks":"[\"cs.LG\",\"quant-ph\"]","methods":"[]","has_code":false}