论文标题

在与噪声相关的实验中见证纠缠

Witnessing Entanglement in Experiments with Correlated Noise

论文作者

Dirkse, Bas, Pompili, Matteo, Hanson, Ronald, Walter, Michael, Wehner, Stephanie

论文摘要

纠缠证人实验的目的是证明从有限数量的试验中创建纠缠状态的目的。这种实验的统计信心通常表示为观察到的证人违规的标准偏差数量。该方法隐含地假设噪声是良好的,因此适用了中心极限定理。在这项工作中,我们提出了两种分析证人实验的方法,在这些方法中,各州可以任意相关的噪声。我们的第一种方法是拒绝实验,在该实验中,我们通过拒绝实验只能产生可分离状态的假设来证明纠缠的创建。我们通过p值量化了统计置信度,可以将其解释为观察到的数据与只能产生可分离状态的假设一致的可能性。因此,一个小的p值意味着对见证的纠缠充满信心。该方法适用于一般证人实验,也可以用来见证真正的多部分纠缠。我们的第二种方法是估计实验,在该实验中,我们估算并构建平均证人值的置信区间。在存在相关噪声的情况下,此置信区间在统计上是严格的。该方法适用于一般估计问题,包括保真度估计。为了考虑系统的测量和随机设置生成错误,我们的模型考虑了设备的缺陷,我们展示了这两种统计分析方法如何影响。最后,我们根据NV中心的模拟说明了使用我们的方法的使用。

The purpose of an entanglement witness experiment is to certify the creation of an entangled state from a finite number of trials. The statistical confidence of such an experiment is typically expressed as the number of observed standard deviations of witness violations. This method implicitly assumes that the noise is well-behaved so that the central limit theorem applies. In this work, we propose two methods to analyze witness experiments where the states can be subject to arbitrarily correlated noise. Our first method is a rejection experiment, in which we certify the creation of entanglement by rejecting the hypothesis that the experiment can only produce separable states. We quantify the statistical confidence by a p-value, which can be interpreted as the likelihood that the observed data is consistent with the hypothesis that only separable states can be produced. Hence a small p-value implies large confidence in the witnessed entanglement. The method applies to general witness experiments and can also be used to witness genuine multipartite entanglement. Our second method is an estimation experiment, in which we estimate and construct confidence intervals for the average witness value. This confidence interval is statistically rigorous in the presence of correlated noise. The method applies to general estimation problems, including fidelity estimation. To account for systematic measurement and random setting generation errors, our model takes into account device imperfections and we show how this affects both methods of statistical analysis. Finally, we illustrate the use of our methods with detailed examples based on a simulation of NV centers.

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