Online Shadow Tomography Matching the Classical Bounds
TL;DR
The key to the proof is a new framework for quantifying post-measurement damage, based on the quantum Efron-Stein decomposition, which improves all three exponents even in the Offline Shadow Tomography setting.
Abstract
In Online Shadow Tomography, we are given copies of an unknown $d$-dimensional quantum state $\rho$, an adversary (adaptively) proposes a sequence of bounded observables $A^{(1)},\ldots,A^{(m)}$, and after each $A^{(t)}$ is given we must estimate $\mathrm{Tr}(A^{(t)}\rho)$ to within $\pm \epsilon$. This is the direct quantum generalization of the classical problem of Adaptive Data Analysis. Prior results for online Shadow Tomography were suboptimal in all three parameters $m, d, \epsilon$, lagging behind the best known and classical rates, for which there is some evidence of optimality. In this work, we finally close this gap, giving a pair of algorithms matching the classical rates. Our first algorithm is the first to achieve $o(\log^2 m)$-dependence together with $\mathrm{poly}(\log(d)/\epsilon)$; moreover, it improves all three exponents even in the Offline Shadow Tomography setting. Our second algorithm is known to be optimal among bounds independent of $d$, and improves the best prior result by a $\sqrt{m} \log m$ factor. The key to our proof is a new framework for quantifying post-measurement damage, based on the quantum Efron-Stein decomposition.