这篇论文用7组模型对比告诉你：现阶段量子机器学习还没跑赢经典方法，但在过滤噪声上有戏。想了解量子ML实际表现可以看看。
该论文对7个模型对（跨越监督学习和强化学习）进行了量子机器学习与经典方法的统一实证比较。结果显示，量子模型在整体预测性能、策略稳定性和训练时间上尚未超越经典基线。不过，量子方法在噪声过滤和假阳性控制上展现了潜力。研究还总结了量子ML在硬件环境、训练效率和收敛稳定性方面的挑战。代码已开源在GitHub。
Quantum vs. Classical Machine Learning: A Unified Empirical Comparison
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.