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arXiv 2003.02181hep-phhep-exphysics.data-anquant-ph
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一种用于模型无关的新物理搜索的量子算法

A Quantum Algorithm for Model-Independent Searches for New Physics

Konstantin T. Matchev, Prasanth Shyamsundar, Jordan Smolinsky

备注：
9 pages, 24 figures. New figures and references added; conclusions unchanged. Matches journal version
发表信息：
LHEP 2023, 301 (2023)
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AI总结：

提出一种量子算法，利用Ising哈密顿量基态能量作为检验统计量，搜索多维分箱对撞机数据中的未建模异常，有效降低别处效应并优于常用拟合优度检验。

摘要：

我们提出了一种新颖的量子技术，用于搜索多维分箱对撞机数据中未建模的异常。我们建议将每个分箱与一个Ising晶格自旋位点关联，Ising哈密顿量由观测数据和相应的理论预期适当构建。为了捕捉数据中的空间相关异常，我们引入了相邻位点之间的自旋-自旋相互作用以及自相互作用。所得Ising哈密顿量的基态能量可作为新的检验统计量，可通过经典计算或绝热量子优化计算。我们证明我们的检验统计量优于一些最常用的拟合优度检验。新方法通过利用统计噪声与真实新物理信号之间的典型差异，大大减少了“别处效应”。

英文摘要：

We propose a novel quantum technique to search for unmodeled anomalies in multidimensional binned collider data. We propose associating an Ising lattice spin site with each bin, with the Ising Hamiltonian suitably constructed from the observed data and a corresponding theoretical expectation. In order to capture spatially correlated anomalies in the data, we introduce spin-spin interactions between neighboring sites, as well as self-interactions. The ground state energy of the resulting Ising Hamiltonian can be used as a new test statistic, which can be computed either classically or via adiabatic quantum optimization. We demonstrate that our test statistic outperforms some of the most commonly used goodness-of-fit tests. The new approach greatly reduces the look-elsewhere effect by exploiting the typical differences between statistical noise and genuine new physics signals.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。

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