Markov chains approximation of jump-diffusion stochastic master equations
Annales de l'I.H.P. Probabilités et statistiques, Volume 46 (2010) no. 4, pp. 924-948.

Quantum trajectories are solutions of stochastic differential equations obtained when describing the random phenomena associated to quantum continuous measurement of open quantum system. These equations, also called Belavkin equations or Stochastic Master equations, are usually of two different types: diffusive and of Poisson-type. In this article, we consider more advanced models in which jump-diffusion equations appear. These equations are obtained as a continuous time limit of martingale problems associated to classical Markov chains which describe quantum trajectories in a discrete time model. The results of this article goes much beyond those of [Ann. Probab. 36 (2008) 2332-2353] and [Existence, uniqueness and approximation for stochastic Schrödinger equation: The Poisson case (2007)]. The probabilistic techniques, used here, are completely different in order to merge these two radically different situations: diffusion and Poisson-type quantum trajectories.

Les trajectoires quantiques sont des solutions d'équations différentielles stochastiques décrivant des phénomènes aléatoires associés aux principes de mesure (quantique) des systèmes quantiques ouverts. Ces équations, également appelées équations de Belavkin ou équations maîtresses stochastiques sont habituellement de deux types: soit diffusif soit de type saut. Dans cet article, nous considérons des modèles plus avancés où des équations de type saut-diffusion apparaissent. Ces équations sont obtenues comme solutions de problèmes de martingales. Ces problèmes de martingales sont obtenus comme limites continus (en temps) à partir de chaînes de Markov classiques décrivant des trajectoires quantiques pour des modèles à temps discret. Les résultats de cet article généralisent ceux de [Ann. Probab. 36 (2008) 2332-2353] et [Existence, uniqueness and approximation for stochastic Schrödinger equation: The Poisson case (2007)]. Ici, les techniques probabilistes utilisés sont complétement différentes afin de pouvoir mixer les deux types d'évolutions: diffusives et poissoniennes.

DOI: 10.1214/09-AIHP330
Classification: 60F99,  60G99,  60H10
Keywords: stochastic master equations, quantum trajectory, Jump-diffusion stochastic differential equation, stochastic convergence, Markov generators, martingale problem
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Pellegrini, Clément. Markov chains approximation of jump-diffusion stochastic master equations. Annales de l'I.H.P. Probabilités et statistiques, Volume 46 (2010) no. 4, pp. 924-948. doi : 10.1214/09-AIHP330. http://www.numdam.org/articles/10.1214/09-AIHP330/

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