Improved Sample Upper and Lower Bounds for Trace Estimation of Quantum State Powers

May 14, 2025 Β· Declared Dead Β· πŸ› Annual Conference Computational Learning Theory

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Authors Kean Chen, Qisheng Wang arXiv ID 2505.09563 Category quant-ph: Quantum Computing Cross-listed cs.IT Citations 5 Venue Annual Conference Computational Learning Theory Last Checked 5 months ago
Abstract
As often emerges in various basic quantum properties such as entropy, the trace of quantum state powers $\operatorname{tr}(ρ^q)$ has attracted a lot of attention. The recent work of Liu and Wang (SODA 2025) showed that $\operatorname{tr}(ρ^q)$ can be estimated to within additive error $\varepsilon$ with a dimension-independent sample complexity of $\widetilde O(1/\varepsilon^{3+\frac{2}{q-1}})$ for any constant $q > 1$, where only an $Ω(1/\varepsilon)$ lower bound was given. In this paper, we significantly improve the sample complexity of estimating $\operatorname{tr}(ρ^q)$ in both the upper and lower bounds. In particular: - For $q > 2$, we settle the sample complexity with matching upper and lower bounds $\widetilde Θ(1/\varepsilon^2)$. - For $1 < q < 2$, we provide an upper bound $\widetilde O(1/\varepsilon^{\frac{2}{q-1}})$, with a lower bound $Ω(1/\varepsilon^{\max\{\frac{1}{q-1}, 2\}})$ for dimension-independent estimators, implying there is only room for a quadratic improvement. Our upper bounds are obtained by (non-plug-in) quantum estimators based on weak Schur sampling, in sharp contrast to the prior approach based on quantum singular value transformation and samplizer.
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