aiqbench analysis
This paper establishes tight lower bounds of $\widetilde{\Omega}(N^2)$ samples and $\widetilde{\Omega}(N)$ queries for estimating fundamental quantum state functionals like Uhlmann fidelity, trace distance, and von Neumann entropy. By resolving these open problems with matching upper bounds, the work confirms the near-optimality of numerous algorithms developed since 2016 within the field of quantum information theory.
Nearly tight lower bounds for estimating quantum functionals: Uhlmann fidelity, trace distance, and von Neumann entropy
arXiv:2608.02600v1 Announce Type: new Abstract: In this paper, we present a unified framework for proving lower bounds for estimating functionals of quantum states. We therefore resolve several open problems by establishing lower bounds that match known upper bounds: we show that it requires $\widetilde{\Omega}(N^2)$ samples to estimate the Uhlmann fidelity, trace distance, and von Neumann entropy. Moreover, they immediately imply matching query lower bounds of $\widetilde{\Omega}(N)$ by quantum sample-to-query lifting. These lower bounds imply the near-optimality of a dozen quantum algorithms since 2016.
Abstract:In this paper, we present a unified framework for proving lower bounds for estimating functionals of quantum states. We therefore resolve several open problems by establishing lower bounds that match known upper bounds: we show that it requires $\widetilde{\Omega}(N^2)$ samples to estimate the Uhlmann fidelity, trace distance, and von Neumann entropy. Moreover, they immediately imply matching query lower bounds of $\widetilde{\Omega}(N)$ by quantum sample-to-query lifting. These lower bounds imply the near-optimality of a dozen quantum algorithms since 2016.
Quantum Physics (quant-ph); Computational Complexity (cs.CC); Information Theory (cs.IT)
arXiv:2608.02600 [quant-ph]
(or arXiv:2608.02600v1 [quant-ph] for this version)
https://doi.org/10.48550/arXiv.2608.02600 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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