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

2025-09-17

AI Summary

Small-sample credit risk assessment in inclusive finance is hindered by data scarcity and severe class imbalance. Method: We propose the first hybrid quantum-classical neural network framework tailored for Noisy Intermediate-Scale Quantum (NISQ) devices. Our approach integrates logistic regression, random forest, and XGBoost for robust feature selection, then constructs a parameter-shift-trained quantum neural network (QNN) to enable end-to-end co-optimization of classical feature engineering and quantum classification. Results: On real-world credit datasets, simulated AUC reaches 0.852 ± 0.027; hardware experiments on superconducting quantum processors achieve 0.88 AUC, with significantly higher recall than state-of-the-art classical models. This work pioneers the deployment of practical quantum machine learning in financial risk management, delivering a theoretically rigorous and engineering-feasible solution for high-stakes, low-data decision-making.

Technology Category

• Machine Learning: Quantum Machine Learning
• Humans and AI: Human-in-the-loop Machine Learning
• Search and Optimization: Mixed Discrete/Continuous Search

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

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.

Problem

• Addressing few-shot credit risk assessment with data scarcity
• Developing hybrid quantum-classical workflow for financial problems
• Applying quantum computing to data-constrained financial scenarios

Innovation

• Hybrid quantum-classical workflow design
• Quantum Neural Network as core classifier
• Deployed on superconducting quantum processor