A Gaussian mean width bound in weighted geometry yields a single-letter strong converse for the classical identification capacity of quantum channels, improving known results for depolarizing, Pauli, erasure, and amplitude damping channels.
Strong converse for identification via quantum channels.IEEE Trans
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Derives matrix concentration inequalities for time-inhomogeneous Markov chains under positive Ollivier-Ricci curvature or Saloff-Coste-Zuniga spectral gap, illustrated on dynamic Bradley-Terry-Luce models.
The paper offers an accessible introduction to matrix concentration inequalities and their applications in computational mathematics.
citing papers explorer
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Gaussian mean width strong converse bound on the classical identification capacity of quantum channels
A Gaussian mean width bound in weighted geometry yields a single-letter strong converse for the classical identification capacity of quantum channels, improving known results for depolarizing, Pauli, erasure, and amplitude damping channels.
-
Matrix concentration inequalities for time-inhomogeneous Markov chains
Derives matrix concentration inequalities for time-inhomogeneous Markov chains under positive Ollivier-Ricci curvature or Saloff-Coste-Zuniga spectral gap, illustrated on dynamic Bradley-Terry-Luce models.
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Applied Random Matrix Theory
The paper offers an accessible introduction to matrix concentration inequalities and their applications in computational mathematics.