AI 中文总结
研究通过贝叶斯分类器利用少量全局快照区分随机量子态的能力，发现以x=m/ln k控制的相变并推导阈值，扩展至低深度、噪声电路及相位随机态。
AI 中文摘要
由一般时间演化产生的量子态在局域上是无特征的：局域测量返回的随机结果对所有态都是相同的。然而，在计算基下的全局快照在区分量子态方面却出人意料地有效。我们探索了贝叶斯分类器利用少量测量结果来区分许多随机量子态的能力。我们识别出分类器区分$k$个候选态与$m$次测量（由变量$x = m / \ln k$控制）的能力存在一个相变，并推导出阈值$x_c$。我们将结果扩展到接近反集中起始的低深度电路，以及噪声电路和相位随机态。
英文摘要
Quantum states generated by generic time evolution are locally featureless: local measurements return random outcomes that are identical for all states. However, global snapshots in the computational basis are surprisingly effective at distinguishing between quantum states. We explore the ability of Bayesian classifiers to discriminate between many random quantum states using a small number of measurement outcomes. We identify a phase transition in the ability of the classifier to distinguish between $k$ candidate states with $m$ shots controlled by the variable $x = m / \ln k$, and derive the threshold value $x_c$. We extend our results to low-depth circuits near the onset of anti-concentration, as well as to noisy circuits and phase-random states.
