Quantum Machine Learning for Secure Financial Forecasting: Mitigating Data Breaches and Adversarial Exploits

Abstract

Quantum Machine Learning (QML) offers a transformative approach to financial forecasting by enhancing predictive accuracy and cybersecurity resilience. This study evaluates QML’s effectiveness using financial market data from Yahoo Finance, comparing Quantum Long Short-Term Memory (QLSTM) to classical LSTM and ARIMA models. Security vulnerabilities were assessed using the IEEE DataPort adversarial attack dataset, while encryption performance was analyzed using Quantum Key Distribution (QKD) data from NIST. Experimental results demonstrate that QLSTM outperforms classical models, achieving lower RMSE (1.82), MAE (1.45), and MSE (3.31), indicating superior forecasting precision. Quantum Support Vector Machines (QSVM) exhibit increased adversarial robustness, limiting accuracy degradation to 11.67% under FGSM attacks and 15.60% under PGD attacks, whereas classical models suffer losses exceeding 24%. QKD provides a substantial security advantage over RSA-4096, achieving a 5.87 bps secure key rate and demonstrating over 100 years of resistance to quantum attacks, reinforcing its role as a next-generation cybersecurity mechanism for financial institutions. Despite these advantages, the adoption of QML in financial forecasting faces challenges related to high computational costs, hardware limitations, and integration complexities. Quantum security frameworks require significant infrastructure investments to ensure scalability and reliability. Financial institutions must prioritize QML investment, integrate quantum security mechanisms, and collaborate with regulatory bodies to establish standardized guidelines for secure financial applications. This study contributes to the growing field of quantum-enhanced financial analytics, highlighting its potential to mitigate cyber threats and improve financial forecasting reliability while addressing existing implementation barriers.