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arXiv 1902.02602quant-ph
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一种有效的量子态层析成像贝叶斯有效区域认证方法

Efficient Bayesian credible-region certification for quantum-state tomography

C. Oh, Y. S. Teo, H. Jeong

摘要:

在一般的量子态层析成像中,标准的Bayesian双域理论通过对状态空间进行大量的Monte~Carlo抽样,然后进行样本剔除,在未知状态的唯一估计上构造一个误差域,从而计算其大小和可信度。这种传统方法通常对于源自大数据集的非常小的误差区域给出可忽略不计的产量。我们提出了一个可操作的重新制定的理论来计算的大小和可信度从区域平均量,在原则上传达这两个属性的行为的信息,作为可扩展区域的变化。接下来我们提出了加速的打了就跑的Monte~Carlo抽样,定制的贝叶斯误差区域的建设,以有效地计算区域平均量,并提供其复杂性估计的量子态。最后,通过将大小理解为区域中两个状态之间的区域平均距离(例如用Hilbert-Schmidt,迹类或Bures距离测量),我们推导出近似公式,以分析估计距离诱导的大小和伪Bloch参数化下的可信度,而无需诉诸任何Monte~Carlo计算。

英文摘要:

Standard Bayesian credible-region theory for constructing an error region on the unique estimator of an unknown state in general quantum-state tomography to calculate its size and credibility relies on heavy Monte~Carlo sampling of the state space followed by sample rejection. This conventional method typically gives negligible yield for very small error regions originating from large datasets. We propose an operational reformulated theory to compute both size and credibility from region-average quantities that in principle convey information about behavior of these two properties as the credible-region changes. We next suggest the accelerated hit-and-run Monte~Carlo sampling, customized to the construction of Bayesian error-regions, to efficiently compute region-average quantities, and provide its complexity estimates for quantum states. Finally by understanding size as the region-average distance between two states in the region (measured for instance with either the Hilbert-Schmidt, trace-class or Bures distance), we derive approximation formulas to analytically estimate both distance-induced size and credibility under the pseudo-Bloch parametrization without resorting to any Monte~Carlo computation.

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