对比7组量子与经典机器学习模型，发现当前量子模型尚未超越经典基线。
Quantum vs. Classical Machine Learning: A Unified Empirical Comparison
- 在监督与强化学习中对比7对量子与经典模型
- 量子模型在预测性能、策略稳定性和训练时间上均未胜出
- 适合关注量子机器学习挑战与噪声过滤的研究者
量子计算作为机器学习的潜在计算范式，可能带来相较于经典方法的计算优势。然而，当前支持量子机器学习（QML）模型优于经典模型的证据仍不充分。为此，本文开展了一项实证研究，比较了涵盖监督学习和强化学习的七对模型。结果表明，所评估的量子机器学习模型在整体预测性能、策略稳定性及训练时间上均未超过经典基线。尽管如此，量子模型在噪声过滤和控制误报方面仍具潜力。本研究总结了量子机器学习在硬件环境、训练效率和收敛稳定性方面面临的关键挑战，为提升其鲁棒性与参数优化提供了基础。相关工作已公开于 https://github.com/Z-537-437/QML。
原文摘要 · Abstract (English)
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.
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