
For any discrete memoryless channel W with equal support condition, we prove that lim(1 − η_JSD(W^⊗n))^{1/n} = BC_min(W), where BC_min is the minimum Bhattacharyya coefficient between output distributions. This establishes that bounded divergences exhibit super-tensorization (η → 1) with rate governed by the Chernoff exponent.
tensorization, Jensen-Shannon divergence, contraction coefficient, Bhattacharyya coefficient, strong data processing inequality, information theory
tensorization, Jensen-Shannon divergence, contraction coefficient, Bhattacharyya coefficient, strong data processing inequality, information theory
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