Quantum Multi-Level Estimation of Functionals of Discrete Distributions
Kean Chen, Minbo Gao, Tongyang Li, Qisheng Wang, Xinzhao Wang

TL;DR
This paper introduces a quantum multi-level estimation framework for functionals of discrete distributions, achieving near-optimal quantum algorithms for estimating $q$-Tsallis entropy with improved query complexities.
Contribution
The authors develop a novel quantum estimation method that partitions probability values into intervals, enabling efficient estimation of functionals like $q$-Tsallis entropy with better query complexity bounds.
Findings
Quantum algorithms for $q>1$ with near-optimal query complexity.
Quantum estimators for $0<q<1$ showing quantum speedup.
First near-optimal quantum estimators for non-integer $q$-entropy.
Abstract
We propose a quantum multi-level estimation framework for a functional of a discrete distribution . We partition the values into logarithmically many intervals whose length decays exponentially. For each interval, we perform non-destructive singular value discrimination to isolate the relevant , enabling adaptive estimation of the partial sum over this interval. Unlike previous variable-time approaches, our method avoids high control overhead and requires only constant extra ancilla qubits. As an application, we present efficient quantum estimators for the -Tsallis entropy of discrete distributions. Specifically: (i) For , we obtain a near-optimal quantum algorithm with query complexity , improving the prior best due to Liu and Wang (SODA 2025;…
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