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arXiv · 2605.06452

Tight Contraction Rates for Primitive Channels under Quantum $f$-Divergences

Matthew Simon Tan·Marco Tomamichel·Ian George
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Abstract

Data-processing inequalities capture the phenomenon that two probability distributions can only become less distinguishable under any common post-processing. For more fine-grained inequalities, one turns to strong data-processing inequality (SDPI) constants, which give the strongest inequalities for a given channel and reference state for a fixed measure of distinguishability. These quantities have been used to quantify the rate at which time-homogeneous Markov chains contract towards a fixed point both in the classical and quantum setting. In this work, we establish that quantum $f$-divergences satisfy a local reverse Pinsker inequality, which implies the asymptotic contraction rate of a primitive channel to its stationary state is upper bounded by the SDPI constant of any non-commutative $\chi^2$-divergence. Using quantum-detailed balance, we establish a sufficient condition for these bounds to be tight. Finally, we apply these results to Petz, Matsumoto, and Hirche-Tomamichel $f$-divergences, establishing new and strengthening previously known results.

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Matthew Simon Tan, Marco Tomamichel, Ian George. 2026-05-07. Tight Contraction Rates for Primitive Channels under Quantum $f$-Divergences. https://arxiv.org/abs/2605.06452

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