Hybrid Quantum-Classical Neural Networks for Few-Shot Credit Risk Assessment
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arXiv
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| Autori principali: | , , , , , , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866916954470612992 |
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| author | Wang, Zheng-an Wang, Yanbo J. Zhang, Jiachi Xu, Qi Zhao, Yilun Li, Jintao Zhang, Yipeng Yang, Bo Gao, Xinkai Cao, Xiaofeng Xu, Kai Hao, Pengpeng Yang, Xuan Fan, Heng |
| author_facet | Wang, Zheng-an Wang, Yanbo J. Zhang, Jiachi Xu, Qi Zhao, Yilun Li, Jintao Zhang, Yipeng Yang, Bo Gao, Xinkai Cao, Xiaofeng Xu, Kai Hao, Pengpeng Yang, Xuan Fan, Heng |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_13818 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Hybrid Quantum-Classical Neural Networks for Few-Shot Credit Risk Assessment Wang, Zheng-an Wang, Yanbo J. Zhang, Jiachi Xu, Qi Zhao, Yilun Li, Jintao Zhang, Yipeng Yang, Bo Gao, Xinkai Cao, Xiaofeng Xu, Kai Hao, Pengpeng Yang, Xuan Fan, Heng Machine Learning Quantum Physics 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. |
| title | Hybrid Quantum-Classical Neural Networks for Few-Shot Credit Risk Assessment |
| topic | Machine Learning Quantum Physics |
| url | https://arxiv.org/abs/2509.13818 |