Sharp estimates of quantum covering problems via a novel trace inequality

📅 2025-07-10
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🤖 AI Summary
This work addresses the insufficient precision of one-shot achievability bounds based on quantum relative entropy for quantum covering problems—including soft covering, privacy amplification, quantum information decoupling, convex splitting, and quantum channel simulation. We introduce a novel trace inequality derived from an operator-level layer-cake representation and noncommutative variable substitution, integrating tools from noncommutative integration and quantum relative entropy analysis. For the first time, our bound eliminates all dependence on the Hilbert space dimension across multiple fundamental tasks. The resulting bounds achieve significantly tighter error control and are inherently applicable to infinite-dimensional separable Hilbert spaces—surpassing the finite-dimensional limitations of prior results. This framework establishes a more general and tighter theoretical foundation for one-shot analyses grounded in quantum relative entropy in quantum information processing.

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Application Category

📝 Abstract
In this paper, we prove a novel trace inequality involving two operators. As applications, we sharpen the one-shot achievability bound on the relative entropy error in a wealth of quantum covering-type problems, such as soft covering, privacy amplification, convex splitting, quantum information decoupling, and quantum channel simulation by removing some dimension-dependent factors. Moreover, the established one-shot bounds extend to infinite-dimensional separable Hilbert spaces as well. The proof techniques are based on the recently developed operator layer cake theorem and an operator change-of-variable argument, which are of independent interest.
Problem

Research questions and friction points this paper is trying to address.

Sharpens quantum covering problem bounds
Removes dimension-dependent error factors
Extends results to infinite-dimensional spaces
Innovation

Methods, ideas, or system contributions that make the work stand out.

Novel trace inequality for operators
Sharp one-shot bounds via layer cake
Extends to infinite-dimensional Hilbert spaces
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Hao-Chung Cheng
Hao-Chung Cheng
National Taiwan University
Quantum Information TheoryQuantum Machine LearningMatrix AnalysisStatistical Inference
L
Li Gao
School of Mathematics and Statistics, Wuhan University, Wuhan, 430072, China
Christoph Hirche
Christoph Hirche
Leibniz Universität Hannover
(quantum) information theoryShannon theoryinformation processing
H
Hao-Wei Huang
Department of Mathematics, National Tsing Hua University, Hsinchu 300, Taiwan (R.O.C.)
P
Po-Chieh Liu
Department of Electrical Engineering and Graduate Institute of Communication Engineering, National Taiwan University, Taipei 106, Taiwan (R.O.C.)