Quantum vs. Classical Machine Learning: A Unified Empirical Comparison

2026-07-01

AI Summary

This study addresses the current lack of systematic empirical comparisons between quantum machine learning (QML) and classical machine learning in terms of performance, training efficiency, and stability. The authors establish a unified experimental framework to evaluate seven pairs of quantum and corresponding classical models across multitask supervised and reinforcement learning settings, using real-world datasets and diverse hardware environments. Their results demonstrate that existing QML models do not yet surpass classical baselines in overall performance, training speed, or stability. However, they reveal that QML exhibits distinctive potential in noise suppression and controlling false positives. This work provides the first systematic characterization of the practical advantages and fundamental challenges facing QML, delineating its current boundaries relative to classical approaches.

Abstract

Quantum computing has emerged as a promising computational paradigm for machine learning (ML), with the potential to offer computational advantages over classical approaches. At this stage, the evidence supporting the performance and advantages of quantum machine learning (QML) models relative to classical models is insufficient.To address this gap, this paper presents an empirical study on the performance of QML models and their classical counterparts. We compare seven model pairs spanning supervised learning and reinforcement learning. Our results indicate that the evaluated quantum machine learning models do not yet surpass the classical baselines in overall prediction performance, policy stability, or training time. Nevertheless, QML remains a promising approach for filtering noise and controlling false positives. Our research findings summarize the challenges facing quantum machine learning across hardware environments, training efficiency, and convergence stability, providing a foundation for research into the robustness and parameter optimization of QML. This work is publicly available at https://github.com/Z-537-437/QML.

Problem

• Quantum Machine Learning
• Classical Machine Learning
• Empirical Comparison
• Performance Evaluation
• Computational Advantage

Innovation

• Quantum Machine Learning
• Empirical Comparison
• Classical Baselines
• Noise Filtering
• Convergence Stability