We investigate the distribution of errors on a computationally useful entangled state generated via the repeated emission from an emitter undergoing strongly non-Markovian evolution. For emitter-environment coupling of pure-dephasing form, we show that the probability that a particular patten of errors occurs has a bound of Markovian form, and thus, accuracy threshold theorems based on Markovia...

We introduce a novel stochastic process algebra, called PHASE, which directly supports phase-type distributions, useful for approximating the dynamics of non-Markovian HCI systems. In order to encourage the effective use of PHASE, we give a step by step account of how PHASE processes can be implemented in PRISM, one of the most powerful and popular probabilistic model checkers currently availab...

Gesture recognition is an important subtask of systems implementing human-machine-interaction. Hidden Markov Models achieve good results for gesture recognition in real-time supporting a low error rate. However, the distinction of gestures with different execution speeds is difficult. Hidden non-Markovian Models provide an approach to model time dependent state transitions to eliminate these pr...

A non-Markovian model of quantum repeated interactions between a small quantum system and an infinite chain of quantum systems is presented. By adapting and applying usual projection operator techniques in this context, discrete versions of the integro-differential and time-convolutioness Master equations for the reduced system are derived. Next, an intuitive and rigorous description of the ind...

Non-Markovian noise presents a particularly relevant challenge in understanding and combating decoherence quantum computers, yet is challenging to capture terms of simple models. Here we show that phenomenological dynamical model known as the post-Markovian master equation (PMME) accurately captures predicts non-Markovian superconducting qubit system. The PMME constructed using experimentally m...

This paper deals with the statistical segmentation of multisensor images. In a Bayesian context, the interest of using hidden Markov random fields, which allows one to take contextual information into account, has been well known for about 20 years. In other situations, the Bayesian framework is insufficient and one must make use of the theory of evidence. The aim of our work is to propose evid...

Humans can learn under a wide variety of feedback conditions. Reinforcement learning (RL), where a series of rewarded decisions must be made, is a particularly important type of learning. Computational and behavioral studies of RL have focused mainly on Markovian decision processes, where the next state depends on only the current state and action. Little is known about non-Markovian decision m...

In this letter, a generalization of pairwise models to non-Markovian epidemics on networks is presented. For the case of infectious periods of fixed length, the resulting pairwise model is a system of delay differential equations (DDE), which shows excellent agreement with results based on explicit stochastic simulations of non-Markovian epidemics on networks. Furthermore, we analytically compu...

We describe how multi-stage non-Markovian decision problems can be solved using actor-critic reinforcement learning by assuming that a discrete version of CohenGrossberg node dynamics describes the node-activation computations of a neural network (NN). Our NN (i.e., agent) is capable of rendering the process Markovian implicitly and automatically in a totally model-free fashion without learning...

In this paper, we analyze and synthesize a multi-server loss system with repeated customers, arising out of NTT DoCoMo-developed telecommunication networks. We first provide the numerical solution for a Markovian model with exponential retrial intervals. Applying Little’s formula, we derive the main system performance measures (blocking probability and mean waiting time) for general non-Markovi...