密度矩阵之间的迹距离：新物理搜索中的一个巧妙工具
The trace distance between density matrices, a nifty tool in new-physics searches
AI总结：
本文将量子信息中的迹距离工具引入高能物理新物理搜索，通过三个实例证明其与截面结合能给出比其他量子观测量更强的标准模型偏离限制，还首次在对撞机数据中确定了魔法且显著性超5σ。
摘要：
量子信息方法已应用于高能物理领域，包括对撞机实验中纠缠和贝尔非定域性的研究。量子信息观测量也被用于限制可能的新物理效应。我们通过引入常用于比较量子态的量子信息工具（迹距离和保真度）改进了这一点。我们发现，迹距离优于文献中考虑的其他量子信息观测量，并且与截面一起，对可能偏离标准模型的情况给出了最强的限制。通过三个新物理搜索的例子证明了所提出方法的效力：第一个涉及顶夸克的色磁偶极矩，得到了利用量子层析和实际实验数据计算的第一个限制；另外两个例子使用蒙特卡洛模拟，对Belle和未来对撞机（假设为LEP3）上τ轻子的反常耦合设定了预期限制。对于这些新物理搜索，我们还比较了迹距离与其他量子信息量（如并发度、魔法和保真度距离）的灵敏度。顺便说一句，我们通过分析LHC上的顶夸克对产生和粲夸克偶素衰变，首次在对撞机数据中确定了魔法，两种情况的显著性都远高于5σ水平。
英文摘要：
Quantum information methods have been brought to bear on high-energy physics, including the study of entanglement and Bell nonlocality in collider experiments. Quantum information observables have also been employed to constrain possible new physics effects. We improve on this point by introducing quantum information tools routinely used to compare quantum states: the trace distance and the fidelity. We find that the former outperforms other quantum information observables considered in the literature and, together with the cross section, yields the strongest bounds on possible departures from the Standard Model. The power of the proposed methodology is demonstrated with three examples of new physics searches. The first concerns the chromomagnetic dipole moment of the top quark and yields the first bound computed by means of quantum tomography and actual experimental data. The other two examples use Monte Carlo simulations and set the projected limits on the anomalous couplings of the $τ$ leptons at Belle and at a future collider, which is taken to be LEP3. For these new physics searches we also compare the sensitivity of the trace distance to those of other quantum information quantities like concurrence, magic, and the fidelity distance. In passing, we provide the first determinations of magic in colliders data by analyzing the top-quark pair production at the LHC and the charmonium decays. The significance is well above the $5σ$ level in both the cases.
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。
