# LEP-QNN: Loan Eligibility Prediction using Quantum Neural Networks

arXiv: https://arxiv.org/abs/2412.03158 · HTML: https://mlsanity.com/paper/2412.03158/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 47 references that are in the map.
- First posted on arXiv 4 Dec 2024.

## Builds on (most cited first, 13 of 13)

- [Quantum Machine Learning](https://mlsanity.com/paper/1611.09347/) (2016)
- [Financial Fraud Detection using Quantum Graph Neural Networks](https://mlsanity.com/paper/2309.01127/) (2023)
- [Dynamic Portfolio Optimization with Real Datasets Using Quantum Processors and Quantum-Inspired Tensor Networks](https://mlsanity.com/paper/2007.00017/) (2020)
- [FedQNN: Federated Learning using Quantum Neural Networks](https://mlsanity.com/paper/2403.10861/) (2024)
- [Financial Fraud Detection: A Comparative Study of Quantum Machine Learning Models](https://mlsanity.com/paper/2308.05237/) (2023)
- [QFNN-FFD: Quantum Federated Neural Network for Financial Fraud Detection](https://mlsanity.com/paper/2404.02595/) (2024)
- [Enhancing Quantum Support Vector Machines through Variational Kernel Training](https://mlsanity.com/paper/2305.06063/) (2023)
- [Simulation of a Variational Quantum Perceptron using Grover's Algorithm](https://mlsanity.com/paper/2305.11040/) (2023)
- [QADQN: Quantum Attention Deep Q-Network for Financial Market Prediction](https://mlsanity.com/paper/2408.03088/) (2024)
- [Quantum State Tomography using Quantum Machine Learning](https://mlsanity.com/paper/2308.10327/) (2023)
- [Financial Risk Management on a Neutral Atom Quantum Processor](https://mlsanity.com/paper/2212.03223/) (2022)
- [AQ-PINNs: Attention-Enhanced Quantum Physics-Informed Neural Networks for Carbon-Efficient Climate Modeling](https://mlsanity.com/paper/2409.01626/) (2024)
- [JustQ: Automated Deployment of Fair and Accurate Quantum Neural Networks](https://mlsanity.com/paper/2403.11048/) (2024)

## Built on by (most cited first, 9 of 9)

- [Comparative Performance Analysis of Quantum Machine Learning Architectures for Credit Card Fraud Detection](https://mlsanity.com/paper/2412.19441/) (2024)
- [QFAL: Quantum Federated Adversarial Learning](https://mlsanity.com/paper/2502.21171/) (2025)
- [RobQFL: Robust Quantum Federated Learning in Adversarial Environment](https://mlsanity.com/paper/2509.04914/) (2025)
- [Quantum vs. Classical Machine Learning: A Benchmark Study for Financial Prediction](https://mlsanity.com/paper/2601.03802/) (2026)
- [IQNN-CS: Interpretable Quantum Neural Network for Credit Scoring](https://mlsanity.com/paper/2510.15044/) (2025)
- [QFDNN: A Resource-Efficient Variational Quantum Feature Deep Neural Networks for Fraud Detection and Loan Prediction](https://mlsanity.com/paper/2504.19632/) (2025)
- [Q-SpiRL: Quantum Spiking Reinforcement Learning for Adaptive Robot Navigation](https://mlsanity.com/paper/2605.20801/) (2026)
- [QPredSGG: Hybrid Quantum Predicate Learning for Long-Tailed Scene Graph Generation](https://mlsanity.com/paper/2606.04689/) (2026)
- [MDQEC-QAS: Meta-Decoding for Quantum Error Correction with Hardware-Aware VQC Search and Confidence-Gated Recovery](https://mlsanity.com/paper/2607.10707/) (2026)

## Abstract (arXiv)

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.