Sublinear quantum algorithms for estimating von Neumann entropy

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 γ>1 of the Shannon entropy of probability distributions and the von Neumann entropy of mixed quantum states. Our main results are: quadbullet an widetilde{mathcal{O}}left\(n^{frac{1+η}{2γ²}}right\)-query quantum algorithm that outputs a γ-multiplicative approximation of the Shannon entropy H\(mathbf{p}\) of a classical probability distribution mathbf{p} = \(p₁,ldots,p_n\); quadbullet an widetilde{mathcal{O}}left\(n^{frac12+frac{1+η}{2γ²}}right\)-query quantum algorithm that outputs a γ-multiplicative approximation of the von Neumann entropy S(ρ) of a density matrix ρinmathbb{C}^{ntimes n}. In both cases, the input is assumed to have entropy bounded away from zero by a quantity determined by the parameter η>0, since, as we prove, no polynomial query algorithm can multiplicatively approximate the entropy of distributions with arbitrarily low entropy. In addition, we provide Ωleft\(n^{frac{1}{3γ²}}right\) lower bounds on the query complexity of γ-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.