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English(EN) Classical and Hybrid Quantum Machine Learning for Trigger-Like Event Selection on CMS Open Data: An Eight-Qubit, PCA-Constrained Benchmark

量子机器学习模型在选定高能物理事件中的应用测试

一篇新研究论文探讨了量子机器学习模型在选定高能物理事件中的应用,特别使用了CMS开放数据。该研究将四种经典机器学习模型与四种混合量子模型进行了比较,评估了它们在二元分类任务上的性能。虽然人工神经网络在经典模型中表现最佳,但量子卷积网络也取得了强劲的成果,证明了在有限量子比特预算内混合量子方法的潜力。 AI

影响 探索了量子机器学习在诸如高能物理等专业科学领域的潜在应用。

排序理由 研究论文,详细介绍了经典和量子机器学习模型的基准比较。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

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量子机器学习模型在选定高能物理事件中的应用测试

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研究论文,详细介绍了经典和量子机器学习模型的基准比较。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.LG TIER_1 English(EN) · Tariq Mahmood, Muhammad Awais Rafique, Talab Hussain, Juan Pablo Perez Aguilar, Alfredo Raya, Muhammad Ahsan ·

    用于CMS公开数据上类似触发器的事件选择的经典和混合量子机器学习:一个八量子比特、PCA约束的基准测试

    arXiv:2608.26224v1 Announce Type: cross Abstract: Event triggering sits at the heart of high-energy physics, where the rare events of interest must be retained while an overwhelming background is discarded under tight latency and bandwidth budgets. This work compares four classic…