Measuring Quantum Entropy

TL;DR

This paper develops algorithms and bounds for efficiently estimating quantum entropy measures, such as von Neumann and Rényi entropy, from multiple copies of an unknown quantum state, revealing fundamental complexity limits.

Contribution

It introduces new algorithms with tight bounds for estimating quantum entropy, and characterizes the copy complexity for different entropy types, advancing understanding of quantum state measurement.

Findings

Algorithms with optimal copy complexity for non-integral α and von Neumann entropy.

Matching lower bounds for integral α > 1 entropy estimation.

Strengthened lower bounds for the Empirical Young Diagram algorithm.

Abstract

The entropy of a quantum system is a measure of its randomness, and has applications in measuring quantum entanglement. We study the problem of measuring the von Neumann entropy, $S(\rho)$, and R\'enyi entropy, $S_\alpha(\rho)$ of an unknown mixed quantum state $\rho$ in $d$ dimensions, given access to independent copies of $\rho$. We provide an algorithm with copy complexity $O(d^{2/\alpha})$ for estimating $S_\alpha(\rho)$ for $\alpha<1$, and copy complexity $O(d^{2})$ for estimating $S(\rho)$, and $S_\alpha(\rho)$ for non-integral $\alpha>1$. These bounds are at least quadratic in $d$, which is the order dependence on the number of copies required for learning the entire state $\rho$. For integral $\alpha>1$, on the other hand, we provide an algorithm for estimating $S_\alpha(\rho)$ with a sub-quadratic copy complexity of $O(d^{2-2/\alpha})$. We characterize the copy complexity for integral $\alpha>1$ up to constant factors by providing matching lower bounds. For other values of $\alpha$, and the von Neumann entropy, we show lower bounds on the algorithm that achieves the upper bound. This shows that we either need new algorithms for better upper bounds, or better lower bounds to tighten the results. For non-integral $\alpha$, and the von Neumann entropy, we consider the well known Empirical Young Diagram (EYD) algorithm, which is the analogue of empirical plug-in estimator in classical distribution estimation. As a corollary, we strengthen a lower bound on the copy complexity of the EYD algorithm for learning the maximally mixed state by showing that the lower bound holds with exponential probability (which was previously known to hold with a constant probability). For integral $\alpha>1$, we provide new concentration results of certain polynomials that arise in Kerov algebra of Young diagrams.