Computational Hardness of Estimating Quantum Entropies via Binary Entropy Bounds
We investigate the computational hardness of estimating the quantum c-Rényi entropy SαR(ρ) (Equation Present) and the quantum q-Tsallis entropy SqT(ρ) (Equation Present), both converging to the von Neumann entropy as the order approaches 1. The promise problems Quantum α-Rényi Entropy Approximation (RényiQEAα) and Quantum q-Tsallis Entropy Approximation (TsallisQEAq) ask whether SRα(ρ) or STq(ρ), respectively, is at least τY or at most τN, where τY − τN is typically a positive constant. Previous hardness results cover only the von Neumann entropy (order 1) and some cases of the quantum q-Tsallis entropy, while existing approaches do not readily extend to other orders. We establish that for all positive real orders, the rank-2 variants Rank2RényiQEAα and Rank2TsallisQEAq are BQP-hard. Combined with prior (rank-dependent) quantum query algorithms in Wang, Guan, Liu, Zhang, and Ying (TIT 2024), Wang, Zhang, and Li (TIT 2024), and Liu and Wang (SODA 2025), our results imply: For all real order α > 0 and 0 < q ≤ 1, LowRankRényiQEAα and LowRankTsallisQEAq are BQP-complete, where both are restricted versions of RényiQEAα and TsallisQEAq with ρ of polynomial rank. For all real order q > 1, TsallisQEAq is BQP-complete. Our hardness results stem from reductions based on new inequalities relating the α-Rényi or q-Tsallis binary entropies of different orders, where the reductions differ substantially from previous approaches, and the inequalities are also of independent interest.
Cite this item
Liu, Y. (2026). Computational Hardness of Estimating Quantum Entropies via Binary Entropy Bounds. In M. Mahajan, F. Manea, A. McIver, & K. Nguyen (Editors), Leibniz International Proceedings in Informatics, LIPIcs (No. 66). 43rd International Symposium on Theoretical Aspects of Computer Science, Grenoble, France, March 9, 2026. https://doi.org/10.4230/LIPIcs.STACS.2026.66
Click Copy to copy the citation, or select the text manually.
LIPIcs.STACS.2026.66.pdf
Main Document
Published version
openaccess
CC BY
1001.11 KB
Adobe PDF
fff64c46c4aaa98ce7f0e396467c1c81