Quantum vs. Classical Machine Learning: A Unified Empirical Comparison
2026-07-01 • Machine Learning
Machine Learning
AI summaryⓘ
The authors looked at how well quantum machine learning (QML) models perform compared to regular, classical machine learning models. They tested seven pairs of models in tasks like supervised learning and reinforcement learning. The study found that the current QML models do not outperform classical models in accuracy, stability, or training speed. However, the authors note that QML shows promise in handling noise and reducing false alarms. Their work highlights challenges like hardware limits and training issues, setting the stage for future improvements in QML.
Quantum computingMachine learningQuantum machine learningSupervised learningReinforcement learningPrediction performancePolicy stabilityTraining efficiencyFalse positivesConvergence stability
Authors
Chuanming Yu, Jiaming Liu, Zihao Ge, Xiongfei Wu, Lulu Zhu, Pengzhan Zhao, Jianjun Zhao
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.