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arXiv 1202.3116quant-ph
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最大熵推论的连续性

Continuity of the Maximum-Entropy Inference

Stephan Weis

摘要:

我们研究了通过最大化一个连续的排序函数,从一组固定的可观测量的期望值推断有限能级量子系统的状态的逆问题。我们之前已经证明,最大熵推理可以是从期望值凸集到状态凸集的不连续映射,因为图像包含支持度减少的状态,而该映射仅限于完全支持状态的吉布斯族的平滑参数化。在这里,我们证明了任意的排名功能,推理是连续的边界点。这是从一个连续性条件的开放性的限制线性映射的状态,以他们的期望值。开放性条件还表明,具有不连续推理的排序函数是典型的。此外,它表明,推论是连续的限制,任何多面体,这意味着一个不连续属于量子域的非交换可观的和一个测地闭包的吉布斯家庭等于一组最大熵的状态。我们讨论了八个描述的最大熵状态的集合的准确性和偏差的分析证明。

英文摘要:

We study the inverse problem of inferring the state of a finite-level quantum system from expected values of a fixed set of observables, by maximizing a continuous ranking function. We have proved earlier that the maximum-entropy inference can be a discontinuous map from the convex set of expected values to the convex set of states because the image contains states of reduced support, while this map restricts to a smooth parametrization of a Gibbsian family of fully supported states. Here we prove for arbitrary ranking functions that the inference is continuous up to boundary points. This follows from a continuity condition in terms of the openness of the restricted linear map from states to their expected values. The openness condition shows also that ranking functions with a discontinuous inference are typical. Moreover it shows that the inference is continuous in the restriction to any polytope which implies that a discontinuity belongs to the quantum domain of non-commutative observables and that a geodesic closure of a Gibbsian family equals the set of maximum-entropy states. We discuss eight descriptions of the set of maximum-entropy states with proofs of accuracy and an analysis of deviations.

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