Sublinear quantum algorithms for estimating von Neumann entropy

TL;DR

This paper introduces sublinear quantum algorithms for efficiently estimating the von Neumann and Shannon entropies of quantum states and probability distributions within a multiplicative factor, establishing both upper and lower bounds on query complexity.

Contribution

It presents the first sublinear quantum algorithms for multiplicative entropy estimation and provides fundamental lower bounds, advancing understanding of quantum entropy approximation.

Findings

Quantum algorithms achieve sublinear query complexity for entropy estimation.

Lower bounds show the limitations of polynomial query algorithms for low-entropy states.

Algorithms work under the most general quantum input model, the purified query access model.

Abstract

Entropy is a fundamental property of both classical and quantum systems, spanning myriad theoretical and practical applications in physics and computer science. We study the problem of obtaining estimates to within a multiplicative factor $\gamma>1$ of the Shannon entropy of probability distributions and the von Neumann entropy of mixed quantum states. Our main results are: $\quad\bullet$ an $\widetilde{\mathcal{O}}\left( n^{\frac{1+\eta}{2\gamma^2}}\right)$-query quantum algorithm that outputs a $\gamma$-multiplicative approximation of the Shannon entropy $H(\mathbf{p})$ of a classical probability distribution $\mathbf{p} = (p_1,\ldots,p_n)$; $\quad\bullet$ an $\widetilde{\mathcal{O}}\left( n^{\frac12+\frac{1+\eta}{2\gamma^2}}\right)$-query quantum algorithm that outputs a $\gamma$-multiplicative approximation of the von Neumann entropy $S(\rho)$ of a density matrix $\rho\in\mathbb{C}^{n\times n}$. In both cases, the input is assumed to have entropy bounded away from zero by a quantity determined by the parameter $\eta>0$, since, as we prove, no polynomial query algorithm can multiplicatively approximate the entropy of distributions with arbitrarily low entropy. In addition, we provide $\Omega\left(n^{\frac{1}{3\gamma^2}}\right)$ lower bounds on the query complexity of $\gamma$-multiplicative estimation of Shannon and von Neumann entropies. We work with the quantum purified query access model, which can handle both classical probability distributions and mixed quantum states, and is the most general input model considered in the literature.