Predicting loan eligibility with high accuracy remains a significant challenge in the finance sector. Accurate predictions enable financial institutions to make informed decisions, mitigate risks, and effectively adapt services to meet customer needs. However, the complexity and the high-dimensional nature of financial data have always posed significant challenges to achieving this level of precision. To overcome these issues, we propose a novel approach that employs Quantum Machine Learning (QML) for Loan Eligibility Prediction using Quantum Neural Networks (LEP-QNN).Our innovative approach achieves an accuracy of 98% in predicting loan eligibility from a single, comprehensive dataset. This performance boost is attributed to the strategic implementation of a dropout mechanism within the quantum circuit, aimed at minimizing overfitting and thereby improving the model's predictive reliability. In addition, our exploration of various optimizers leads to identifying the most efficient setup for our LEP-QNN framework, optimizing its performance. We also rigorously evaluate the resilience of LEP-QNN under different quantum noise scenarios, ensuring its robustness and dependability for quantum computing environments. This research showcases the potential of QML in financial predictions and establishes a foundational guide for advancing QML technologies, marking a step towards developing advanced, quantum-driven financial decision-making tools.

翻译：在金融领域，高精度预测贷款资格仍是一项重大挑战。准确的预测能使金融机构做出明智决策、降低风险，并有效调整服务以满足客户需求。然而，金融数据的复杂性和高维特性一直对实现此精度水平构成显著挑战。为克服这些问题，我们提出了一种新颖方法，即采用量子机器学习（QML）通过量子神经网络进行贷款资格预测（LEP-QNN）。我们的创新方法在单一综合数据集上预测贷款资格的准确率达到98%。这一性能提升归因于在量子电路中策略性地实施了丢弃机制，旨在最小化过拟合并从而提高模型的预测可靠性。此外，通过对多种优化器的探索，我们确定了LEP-QNN框架最高效的配置，从而优化了其性能。我们还严格评估了LEP-QNN在不同量子噪声场景下的鲁棒性，确保其在量子计算环境中的稳健性和可靠性。本研究展示了QML在金融预测中的潜力，并为推进QML技术提供了基础性指导，标志着向开发先进的量子驱动金融决策工具迈出了一步。