2015/11/09 by Hao-Chung Cheng, Min-Hsiu Hsieh, Marco Tomamichel
Computer Science · Mathematics · Physics and Astronomy · #Markov chain #Markov process #Mixing (physics) #Quantum #Quantum Information and Cryptography #Quantum algorithm #Quantum chaos #Quantum many-body systems #Quantum operation #Quantum process #Quantum system #Rate of convergence #Spectral Theory in Mathematical Physics #cs.IT #math-ph #math.IT #math.MP #math.OA #math.PR #quant-ph
paper · pdf · doi:10.1063/1.5000846
published as Journal of Mathematical Physics, 58(9), 092202, Sep 2017
arxiv created 2015/11/09 · openalex created_date 2016/06/24 · openalex publication_date 2017/09/01 · arxiv updated 2019/05/06 · openalex updated_date 2026/08/05
In this work, we extend the theory of quantum Markov processes on a single quantum state to a broader theory that covers Markovian evolution of an ensemble of quantum states, which generalizes Lindblad’s formulation of quantum dynamical semigroups. Our results establish the equivalence between an exponential decrease of the matrix Φ-entropies and the Φ-Sobolev inequalities, which allows us to characterize the dynamical evolution of a quantum ensemble to its equilibrium. In particular, we study the convergence rates of two special semigroups, namely, the depolarizing channel and the phase-damping channel. In the former, since there exists a unique equilibrium state, we show that the matrix Φ-entropy of the resulting quantum ensemble decays exponentially as time goes on. Consequently, we obtain a stronger notion of monotonicity of the Holevo quantity—the Holevo quantity of the quantum ensemble decays exponentially in time and the convergence rate is determined by the modified log-Sobolev inequalities. However, in the latter, the matrix Φ-entropy of the quantum ensemble that undergoes the phase-damping Markovian evolution generally will not decay exponentially. There is no classical analogy for these different equilibrium situations. Finally, we also study a statistical mixing of Markov semigroups on matrix-valued functions. We can explicitly calculate the convergence rate of a Markovian jump process defined on Boolean hypercubes and provide upper bounds to the mixing time.