Selective Quantum State Tomography

TL;DR

The paper introduces selective quantum state tomography (SQST), a method that efficiently estimates specific elements of an unknown quantum state with reduced resource requirements, enabling flexible data extraction for various operators.

Contribution

It presents SQST, a novel tomography scheme that reduces the number of state copies and computational costs, achieving near-optimal bounds for targeted quantum state estimation.

Findings

Uses $O(rac{1}{ ext{ extepsilon}^2} ext{log} d)$ state copies for error $ extepsilon$

Requires fewer resources than traditional methods for partial state estimation

Enables generation of a fixed-size universal data sample for operator expectation values

Abstract

We introduce the concept of selective quantum state tomography or SQST, a tomographic scheme that enables a user to estimate arbitrary elements of an unknown quantum state using a fixed measurement record. We demonstrate how this may be done with the following notable advantages (i) a number of state copies that depends only on the desired precision of the estimation, rather than the dimension of the unknown state; (ii) a similar reduction in the requisite classical memory and computational cost; (iii) an approach to state tomography using $O(\epsilon^{-2}\log d)$ state copies for maximum norm error $\epsilon$, as well as achieving nearly optimal bounds for full tomography with independent measurements. As an immediate extension to this technique we proceed to show that SQST can be used to generate an universal data sample, of fixed and dimension independent size, from which one can extract the mean values from a continuous class of operators on demand.