Quantum Query Complexity of Entropy Estimation

Quantum Query Complexity of Entropy Estimation
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熵估计的量子查询复杂度

DOI:
10.1109/tit.2018.2883306
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发表时间:
2019
影响因子:
2.5
通讯作者:
Wu, Xiaodi
Wu, Xiaodi
中科院分区:
计算机科学2区
文献类型:
--
作者:
Li, Tongyang;Wu, Xiaodi

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未知离散分布的Shannon和Rényi熵的估计是统计性质检验中的一个基本问题。在这篇文章中,我们给出了估计α-Rényi熵(Shannon熵为1-Rényi)的第一个量子算法。特别地,我们证明了香农熵估计的二次量子加速比和α-Rényi熵估计的一般量子加速比对于所有的α≥0值,包括香农熵、哈特利熵(α=0)和碰撞熵(α=2)的紧界。我们还给出了估计最小熵(α=+∞)和Kullback-Leibler散度的量子上界。我们用α-Rényi熵估计的量子下界来补充我们的结果,所有的α≥0值。我们的方法的灵感来自Bravyi,Harrow和Hassidim(BHH)的开创性工作;然而,有了许多新的技术成分:1)我们通过微调的误差分析结合Montanaro的方法来估计α=0,1的量子子例程的期望输出,从而改善了BHH框架的误差相关性;2)我们针对一般的α≥0开发了一个类似于模拟退火中的冷却时间表的过程,以及3)在整数α≥2和α=+∞的情况下,我们分别将熵估计问题归结为α-区分性和[logn]-区分性问题。
Estimation of Shannon and Rényi entropies of unknown discrete distributions is a fundamental problem in statistical property testing. In this paper, we give the first quantum algorithms for estimating α-Rényi entropies (Shannon entropy being 1-Rényi entropy). In particular, we demonstrate a quadratic quantum speedup for Shannon entropy estimation and a generic quantum speedup for α-Rényi entropy estimation for all α ≥ 0 values, including tight bounds for the Shannon entropy, the Hartley entropy (α = 0), and the collision entropy (α = 2). We also provide quantum upper bounds for estimating min-entropy (α = +∞) as well as the Kullback-Leibler divergence. We complement our results with quantum lower bounds on α-Rényi entropy estimation for all α ≥ 0 values. Our approach is inspired by the pioneering work of Bravyi, Harrow, and Hassidim (BHH); however, with many new technical ingredients: 1) we improve the error dependence of the BHH framework by a fine-tuned error analysis together with Montanaro's approach to estimating the expected output of quantum subroutines for α = 0, 1; 2) we develop a procedure, similar to cooling schedules in simulated annealing, for general α ≥ 0, and 3) in the cases of integer α ≥ 2 and α = +∞, we reduce the entropy estimation problem to the α-distinctness and [log n]-distinctness problems, respectively.
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