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arXiv 2109.07019quant-ph
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观测量的粗粒化

Coarse-Graining of Observables

Stan Gudder

摘要:

我们首先定义随机核的概率测度的粗粒化。我们定义了一个概率测度何时是另一个概率测度的一部分,并说两个概率测度共存,如果它们都是一个概率测度的一部分。然后,我们表明,任何两个概率措施共存。我们将这些概念扩展到观测量和仪器,并提到两个观测量不需要共存。我们定义的离散化的一个观察作为一个特殊的情况下,粗粒度,并表明这些\zeroone随机内核。接下来我们考虑有限可观测量和仪器,并表明在这些情况下,随机核被随机矩阵取代。我们还表明,粗粒化是相同的后处理在这种有限的情况下。然后,我们考虑观测量的序列产品,并讨论后处理的观测与另一个观测的序列产品。我们简要地讨论了SIC观测量和量子比特观测量的例子。

英文摘要:

We first define the coarse-graining of probability measures in terms of stochastic kernels. We define when a probability measure is part of another probability measure and say that two probability measures coexist if they are both parts of a single probability measure. We then show that any two probability measures coexist. We extend these concepts to observables and instruments and mention that two observables need not coexist. We define the discretization of an observable as a special case of coarse-graining and show that these have \zeroone stochastic kernels. We next consider finite observables and instruments and show that in these cases, stochastic kernels are replaced by stochastic matrices. We also show that coarse-graining is the same as post-processing in this finite case. We then consider sequential products of observables and discuss the sequential product of a post-processed observable with another observable. We briefly discuss SIC observables and the example of qubit observables.

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。

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