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

Abstract

High-frequency trading (HFT) has emerged as one of the most transformative paradigms in contemporary financial markets, driven by the proliferation of algorithmic trading systems, electronic exchanges, and high-speed communication networks. HFT strategies exploit millisecond-to-microsecond market inefficiencies by executing large volumes of trades at extremely low latencies, seeking incremental profit opportunities that are often invisible to traditional traders (Aldridge, 2013; Fatunmbi et al., 2022). These strategies rely on continuous monitoring of multiple financial instruments, rapid order book analysis, and the integration of diverse market signals, including price movements, trade volumes, order imbalances, and momentum indicators. The real-time, high-dimensional nature of these datasets presents significant computational and analytical challenges, particularly when attempting to maintain predictive accuracy while ensuring sub-millisecond decision-making speeds.

Traditional algorithmic approaches to HFT, such as classical machine learning models, face inherent limitations when processing the vast data streams typical of modern exchanges. For instance, classical Support Vector Machines (SVMs) and other kernel-based methods, while effective in high-dimensional feature spaces, encounter scalability issues due to the computational complexity of kernel matrix evaluations and optimization in extremely large datasets (Fatunmbi et al., 2022). Moreover, the non-linear, highly stochastic nature of financial markets often reduces the efficacy of classical models, necessitating more sophisticated approaches capable of capturing intricate market dependencies and temporal correlations.

Quantum machine learning (QML) offers a promising avenue to overcome these computational bottlenecks by leveraging the principles of quantum mechanics, including superposition, entanglement, and interference, to perform calculations that are intractable for classical systems (Havlíček et al., 2019; Farhi et al., 2014). Specifically, Quantum Support Vector Machines (QSVMs) extend classical SVMs by embedding input data into high-dimensional Hilbert spaces through quantum feature maps, enabling the efficient computation of complex kernel functions. This approach has the potential to enhance predictive performance, accelerate model training, and improve the capacity to detect subtle patterns in high-frequency financial data.

In this study, we undertake a systematic investigation of QSVMs for high-frequency trading strategies. We begin by outlining the theoretical foundations of QSVMs, including quantum kernel constructions, dual optimization formulations, and hybrid classical-quantum implementations suitable for near-term quantum devices. Subsequently, we implement QSVMs on high-dimensional, simulated trading datasets that emulate real-world HFT environments, incorporating multiple assets, temporal features, and market microstructure signals. The performance of QSVMs is rigorously compared to classical SVMs across multiple dimensions, including convergence speed, classification accuracy, predictive reliability, and computational overhead.

Additionally, we explore practical considerations for integrating QSVMs into HFT systems. These include hardware constraints of near-term quantum devices, latency challenges in real-time execution, interpretability of quantum model outputs for risk management, and regulatory compliance. We also discuss the potential benefits of hybrid classical-quantum pipelines that leverage quantum feature mapping while maintaining classical optimization for operational feasibility.

Our results indicate that QSVMs, particularly when employed in hybrid architectures, provide substantial improvements in classification accuracy and model robustness under high-dimensional, noisy trading environments. The study highlights the potential of QSVMs to revolutionize HFT by enabling more precise, faster, and risk-aware trading decisions. These findings lay the groundwork for the development of next-generation quantum-enabled trading systems capable of handling the ever-increasing complexity and speed of modern financial markets while maintaining interpretability and regulatory compliance (Fatunmbi, 2023; Havlíček et al., 2019).