Time-Efficient Quantum Entropy Estimator via Samplizer
arXiv:2401.09947 · doi:10.1109/TIT.2025.3576137
Abstract
Entropy is a measure of the randomness of a system. Estimating the entropy of a quantum state is a basic problem in quantum information. In this paper, we introduce a time-efficient quantum approach to estimating the von Neumann entropy and Rényi entropy of an -dimensional quantum state , given access to independent samples of . Specifically, we provide the following: 1. A quantum estimator for with time complexity , improving the prior best time complexity by Acharya, Issa, Shende, and Wagner (2020) and Bavarian, Mehraba, and Wright (2016). 2. A quantum estimator for with time complexity for and for , improving the prior best time complexity for and for by Acharya, Issa, Shende, and Wagner (2020), though at a cost of a slightly larger sample complexity. Moreover, these estimators are naturally extensible to the low-rank case. We also provide a sample lower bound for estimating . Technically, our method is quite different from the previous ones that are based on weak Schur sampling and Young diagrams. At the heart of our construction, is a novel tool called samplizer, which can "samplize" a quantum query algorithm to a quantum algorithm with similar behavior using only samples of quantum states; this suggests a unified framework for estimating quantum entropies. Specifically, when a quantum oracle block-encodes a mixed quantum state , any quantum query algorithm using queries to can be samplized to a -close (in the diamond norm) quantum algorithm using samples of . Moreover, this samplization is proven to be optimal, up to a polylogarithmic factor.
Final version. 60 pages, 1 table, 13 algorithms, 5 figures. Minor corrections and more discussions. [v2]: Minor modification on the definition of samplizer
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