English(EN) Comparing Classical and Quantum Machine Learning for Regression in High Energy Physics Collision Data

量子机器学习模型在物理数据回归中展现参数效率

作者 PulseAugur 编辑部 · [1 个来源] · 2026-08-31 04:00

研究人员系统地比较了经典机器学习模型（如CNN和LSTM）与量子对应模型（QCNN、QLSTM）在高能物理碰撞数据回归任务上的表现。虽然经典模型目前性能略好，但量子模型在参数效率方面显示出显著优势，一个QCNN所需的参数远少于深度经典CNN即可达到可比的准确率。这项研究为未来在实际量子硬件上的研究提供了基准，突出了资源受限条件下的权衡。 AI

排序理由比较经典与量子机器学习模型的学术论文。[lever_c_research降级：ic=1 ai=1.0]

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arXiv cs.LG TIER_1 English(EN) · Tariq Mahmood, Zain ul Abidin, Itzel Luviano Soto, Alfredo Raya · 2026-08-31 04:00

高能物理碰撞数据中用于回归的经典与量子机器学习的比较

arXiv:2608.28084v1 Announce Type: new Abstract: The classification and regression of particle collision events constitute a persistent computational challenge in experimental high energy physics, where large volumes of simulated data must be processed with both speed and precisio…

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高能物理碰撞数据中用于回归的经典与量子机器学习的比较

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