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 Cram\'er-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.
翻译:我们推导出一种渐进下界,即当N个依赖于未知参数向量的相同量子系统以任意方式进行联合测量,并根据所得数据估计感兴趣的参数时,贝叶斯风险的下限。该下限是由Holevo(1982)提出的量子Cram\'er-Rao下界的一个积分版本,并因此将通常在物理学文献中追求的固定N的确切贝叶斯最优性与在经典数学统计中偏重的点态渐近最优性联系起来。通过启发式论证,可以预期该下界是尖锐的。这在各种重要的示例中确实是正确的,可以用来证明有趣且有用的测量和估计方案的渐进最优性。在此过程中,我们获得了一组新的“双重Holevo下界”,这是一项独立的价值。