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Neural Computing and Applications

A comprehensive empirical evaluation of quantum machine learning for financial prediction: systematic comparison with classical approaches

Raed Abu Zitar, Samia Loucif, Murad M. Al-Rajab, Ahmad Nawaf Nasayreh

Published Oct 1, 2026• 0 citations
Publisher
DOI
10.1007/s00521-026-12516-y

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High dropout and attrition rates among students and employees present critical challenges for educational institutions and corporations, with significant financial and social implications. Traditional machine learning approaches often struggle with imbalanced, high-dimensional data and frequently lack the interpretability necessary for actionable decision-making. This paper presents a comprehensive empirical comparison of hybrid quantum–classical machine learning models against state-of-the-art classical methods for dropout prediction across two distinct real-world domains: academic student performance and corporate employee attrition. We implement a Variational Quantum Classifier (VQC) in PennyLane and benchmark it against Random Forest, XGBoost, and BiLSTM-CNN using identical preprocessing protocols on academic (480 samples) and corporate (1,470 samples) datasets. Results demonstrate problem-dependent quantum advantage: BiLSTM-CNN achieves highest performance on student data (F1: 0.81 vs. VQC’s 0.75), while the tuned VQC outperforms classical methods on employee data (F1: 0.51 vs. BiLSTM-CNN’s 0.48). Integration of SHAP-based explainability enables transparent feature attribution and personalized intervention generation. This work contributes the first systematic empirical benchmark of quantum machine learning for dropout prediction and demonstrates that quantum advantage in the NISQ era is highly problem-dependent rather than universal.
The growing complexity, interdependencies, and rapid fluctuations inherent in modern financial markets create substantial challenges for accurate forecasting, portfolio optimization, and risk management. Conventional machine learning techniques, while powerful, often face limitations in capturing nonlinear relationships and processing high-dimensional datasets efficiently. Quantum machine learning (QML) has emerged as a promising paradigm that leverages quantum computing principles to enhance predictive modeling in finance. This study presents a comprehensive investigation into the application of QML methods—including variational quantum circuits, quantum kernel estimation, and quantum-enhanced support vector machines—for financial time-series prediction and asset price classification. We propose a hybrid quantum-classical framework that integrates quantum feature mapping with classical optimizers to improve model expressiveness and convergence. Empirical experiments are conducted using historical stock market data and synthetic datasets to benchmark QML approaches against established classical models such as long short-term memory networks and gradient boosting machines. The results demonstrate that QML techniques can achieve superior prediction accuracy and lower computational latency under certain data regimes, particularly when dealing with small-to-medium-sized datasets and high feature correlations. Additionally, the study examines scalability considerations, hardware constraints of near-term quantum devices, and the interpretability of quantum model outputs in financial decision-making contexts. The findings underscore the transformative potential of quantum machine learning as an innovative tool for advancing predictive analytics in finance and provide practical insights into how financial institutions can begin integrating QML capabilities into their workflows.