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arXiv math/0512443math.STquant-phstat.TH
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贝叶斯与逐点量子态估计的调和:量子统计中的渐近信息界

Conciliation of Bayes and Pointwise Quantum State Estimation: Asymptotic information bounds in quantum statistics

Richard D. Gill

AI总结:

该研究推导了量子系统参数估计中贝叶斯风险的渐近下界,其为Holevo量子Cramér-Rao界的积分形式,可调和贝叶斯与逐点量子态估计框架,且在多场景下紧,还得到了新的对偶Holevo界。

摘要:

我们推导了当N个状态依赖于未知参数向量的全同量子系统以任意方式联合测量、并基于所得数据估计目标参数时,贝叶斯风险的一个渐近下界。该下界是Holevo(1982)提出的量子Cramér-Rao界的积分形式,由此将物理学文献中通常追求的固定N精确贝叶斯最优性,与经典数理统计中青睐的逐点渐近最优性关联起来。通过启发式论证,可预期该下界是紧的。这一点在多个重要示例中得到证实,它可用于证明一些有意义且实用的测量-估计方案的渐近最优性。研究过程中我们还得到了一类具有独立研究价值的新“对偶Holevo界”。

英文摘要:

We derive an asymptotic lower bound on the Bayes risk when N identical quantum systems whose state depends on a vector of unknown parameters are jointly measured in an arbitrary way and the parameters of interest estimated on the basis of the resulting data. The bound is an integrated version of a quantum Cramér-Rao bound due to Holevo (1982), and it thereby links the fixed N exact Bayesian optimality usually pursued in the physics literature with the pointwise asymptotic optimality favoured in classical mathematical statistics. By heuristic arguments the bound can be expected to be sharp. This does turn out to be the case in various important examples, where it can be used to prove asymptotic optimality of interesting and useful measurement-and-estimation schemes. On the way we obtain a new family of "dual Holevo bounds" of independent interest.

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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