The Keyl-Werner algorithm is not optimal for spectrum estimation

Abstract

We give an algorithm which, given $n = O(d^2 \cdot (\log\log(d)/\log(d))^2)$ copies of $ρ$, estimates the eigenvalues of $ρ$ to constant error in total variation distance. Thus, we can learn the eigenvalues of a quantum state with fewer copies than the $Θ(d^2)$ needed to run full state tomography. This is the first improvement to spectrum estimation over the influential Keyl-Werner algorithm, which uses $n = Θ(d^2)$ copies, thereby resolving a question raised by Keyl and Werner in 2001 and refuting a 2016 conjecture of Wright. Our main technical tool is a new tomography guarantee, where the error of tomography in a particular direction $|w\rangle$ scales with $\langle w | ρ|w\rangle$ for all directions simultaneously. From this stronger "relative-error" bound, we recover better algorithms for principal component analysis in Bures distance and tomography in $χ^2$-divergence as corollaries.