Tight Contraction Rates for Primitive Channels under Quantum $f$-Divergences
This work provides a unified framework for deriving tight contraction rates in quantum Markov chains, benefiting researchers in quantum information theory and statistical physics.
The authors prove that quantum f-divergences satisfy a local reverse Pinsker inequality, which bounds the asymptotic contraction rate of a primitive quantum channel to its stationary state by the SDPI constant of any non-commutative χ²-divergence. They provide a sufficient condition for tightness and apply the results to several specific divergences, strengthening known bounds.
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 $χ^2$-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.