通过量子编码数据分析探索中微子物理的新途径
New Pathways in Neutrino Physics via Quantum-Encoded Data Analysis
AI总结：
本文提出利用量子压缩技术存储更多信息，通过量子编码分析中微子事件，实现对中微子类型分类，有望解决粒子物理学中的街灯效应问题。
摘要：
随着粒子探测器为揭示自然定律而产生越来越多的数据，大量数据率需要使用专门的触发器迅速将数据率降低到可管理的水平；然而，在此过程中，意外的新现象可能会被遗漏。此外，大量数据率越来越难以有效分析，这导致了最近机器学习技术的革命。本文提出了一种基于最新量子压缩技术的方法，能够存储比经典方法多指数级的信息量。为了证明这一点，我们使用8个量子比特的IBM量子处理器，将中微子望远镜事件信息编码为奇偶可观察量。然后我们展示可以以84%的保真度从量子计算机中恢复存储的信息。最后，我们通过执行一个分类任务来说明我们的协议的使用，该任务将电子中微子事件与缪子中微子事件分开。这种新能力最终将允许我们解决粒子物理学中的街灯效应，即我们只记录我们熟悉的粒子的特征。
英文摘要：
Ever-increasing amount of data is produced by particle detectors in their quest to unveil the laws of Nature. The large data rate requires the use of specialized triggers that promptly reduce the data rate to a manageable level; however, in doing so, unexpected new phenomena may escape detection. Additionally, the large data rate is increasingly difficult to analyze effectively, which has led to a recent revolution on machine learning techniques. Here, we present a methodology based on recent quantum compression techniques that has the capacity to store exponentially more amount of information than classically available methods. To demonstrate this, we encode the full neutrino telescope event information using parity observables in an IBM quantum processor using 8 qubits. Then we show that we can recover the information stored on the quantum computer with a fidelity of 84%. Finally, we illustrate the use of our protocol by performing a classification task that separates electron-neutrino events to muon-neutrinos events in a neutrino telescope. This new capability would eventually allow us to solve the street light effect in particle physics, where we only record signatures of particles with which we are familiar.
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