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arXiv 1503.03100quant-phmath.STstat.TH
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极小极大量子层析成像:精确度的极限

Minimax quantum tomography: the ultimate bounds on accuracy

Christopher Ferrie, Robin Blume-Kohout

摘要:

极小极大估计量在最坏情况下具有最小可能误差(“风险”)。我们构造了具有相对熵风险的量子态层析成像的第一个极小极大估计。非自适应断层扫描的极大极小风险标度为$O(1/\sqrt{N})$,而经典概率估计的极大极小风险标度为$O(1/N)$。我们将这一缺陷追溯到采样不匹配:确定风险的未来观测可能来自于与确定估计的过去数据不同的样本空间。这使得极大极小估计非常有偏见,我们提出了一个计算上易于处理的替代方案,在最坏的情况下具有类似的行为,但在大多数状态下具有更高的精度。

英文摘要:

A minimax estimator has the minimum possible error ("risk") in the worst case. We construct the first minimax estimators for quantum state tomography with relative entropy risk. The minimax risk of non-adaptive tomography scales as $O(1/\sqrt{N})$, in contrast to that of classical probability estimation which is $O(1/N)$. We trace this deficiency to sampling mismatch: future observations that determine risk may come from a different sample space than the past data that determine the estimate. This makes minimax estimators very biased, and we propose a computationally tractable alternative with similar behavior in the worst case, but superior accuracy on most states.

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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