A Comparative Study of Classical and Quantum Machine Learning for Large-Scale Financial Forecasting

Abstract

Financial forecasting at large scale covering cross-asset returns, volatility surfaces, and high-frequency microstructure remains a central and challenging problem for both academia and industry. Classical machine learning (ML) and deep learning methods (e.g., gradient-boosted trees, recurrent and attention-based networks) have delivered notable advances in predictive accuracy and trading performance, but they face limits in sample efficiency, feature expressivity for extremely high-dimensional inputs, and in solving certain kernel-like inference problems at scale. Quantum machine learning (QML) techniques principally quantum kernel methods and variational quantum circuits (quantum neural networks, QNNs) offer alternative inductive biases and potentially new computational primitives that may be relevant for financial forecasting, particularly in small-label/high-dimension regimes or combinatorial subproblems such as portfolio optimization.

This manuscript presents a comprehensive comparative study framework for classical and quantum ML approaches applied to large-scale financial forecasting. We (1) survey theoretical foundations, (2) propose reproducible experimental and evaluation protocols, (3) delineate architecture patterns and practical engineering considerations for both classical and quantum approaches, (4) prescribe robust backtesting and risk-aware evaluation metrics, and (5) discuss empirical expectations, limitations, and a prioritized research agenda. We include implementation pseudocode, data preprocessing recommendations, hyperparameter tuning strategies, and considerations for quantum hardware constraints (NISQ devices).