论文标题

页面曲线和典型的线性光学纠缠

Page curves and typical entanglement in linear optics

论文作者

Iosue, Joseph T., Ehrenberg, Adam, Hangleiter, Dominik, Deshpande, Abhinav, Gorshkov, Alexey V.

论文摘要

玻色石高斯状态是无限尺寸希尔伯特空间中的特殊量子状态,与通用连续可变量子计算以及近期量子采样任务(如高斯玻色子采样)有关。在这项工作中,我们研究了一组由随机线性光学统一发展的挤压模式中的纠缠。我们首先得出在Rényi-2页曲线的模式数量(纯波索尼亚高斯状态的子系统的平均rényi-2熵)和相应的页面校正(子系统的平均信息)中,这些公式在某些压缩方案中均不准确。然后,我们证明了关于纠缠的典型性,通过研究其方差来衡量纠缠的典型性。使用上述rényi-2熵的结果,我们的上和下限是von Neumann熵页曲线,并证明了von Neumann Entropy测量的某些纠缠典型性。我们的主要证明利用了对对称属性,该特性符合平均水平和熵的差异,这显着简化了对单位的平均值。从这个角度来看,我们提出了未来的研究方向,可以利用这种对称性。最后,我们讨论了我们的结果的潜在应用及其对高斯玻色子采样的概括,并阐明了纠缠与计算复杂性之间的关系。

Bosonic Gaussian states are a special class of quantum states in an infinite dimensional Hilbert space that are relevant to universal continuous-variable quantum computation as well as to near-term quantum sampling tasks such as Gaussian Boson Sampling. In this work, we study entanglement within a set of squeezed modes that have been evolved by a random linear optical unitary. We first derive formulas that are asymptotically exact in the number of modes for the Rényi-2 Page curve (the average Rényi-2 entropy of a subsystem of a pure bosonic Gaussian state) and the corresponding Page correction (the average information of the subsystem) in certain squeezing regimes. We then prove various results on the typicality of entanglement as measured by the Rényi-2 entropy by studying its variance. Using the aforementioned results for the Rényi-2 entropy, we upper and lower bound the von Neumann entropy Page curve and prove certain regimes of entanglement typicality as measured by the von Neumann entropy. Our main proofs make use of a symmetry property obeyed by the average and the variance of the entropy that dramatically simplifies the averaging over unitaries. In this light, we propose future research directions where this symmetry might also be exploited. We conclude by discussing potential applications of our results and their generalizations to Gaussian Boson Sampling and to illuminating the relationship between entanglement and computational complexity.

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