Investigating the Performance of Quantum Support Vector Machines for High-Frequency Trading Strategies

Abstract

Abstract
High-frequency trading (HFT) strategies operate under extreme requirements of latency, high-dimensional data streams, and rapid decision-making. Classical machine learning models, including support vector machines (SVMs), are widely used in algorithmic trading, but they face limitations when confronted with ultra-high dimensionality, non-stationarity, and the need for near-real-time inference. In this manuscript we investigate the use of quantum support vector machines (QSVMs) a quantum‐machine-learning adaptation of the classical SVM within an HFT setting. We develop a detailed mathematical formulation of the QSVM, embed it into a prototypical HFT pipeline, and perform numerical experiments on tick-level and limit‐order‐book data to compare QSVM against classical SVM counterparts. We report metrics on classification accuracy, decision latency, model training and inference scalability, and robustness to noise and decoherence in near‐term quantum (NISQ) settings. Our results indicate that while QSVMs currently do not universally dominate classical SVMs in all trading settings, they show promise in handling very high‐dimensional feature spaces with competitive latency and accuracy under certain constraints. We conclude with an industry‐oriented discussion of practical implementation issues (hardware access, latency budgets, regulatory considerations) and future research directions.