Search results for "Markov processe"

showing 6 items of 16 documents

Dynamics of correlations due to a phase noisy laser

2012

We analyze the dynamics of various kinds of correlations present between two initially entangled independent qubits, each one subject to a local phase noisy laser. We give explicit expressions of the relevant quantifiers of correlations for the general case of single-qubit unital evolution, which includes the case of a phase noisy laser. Although the light field is treated as classical, we find that this model can describe revivals of quantum correlations. Two different dynamical regimes of decay of correlations occur, a Markovian one (exponential decay) and a non-Markovian one (oscillatory decay with revivals) depending on the values of system parameters. In particular, in the non-Markovia…

Physics03.67.Mn Entanglement measures witnesses and other characterizationQuantum discordQuantum PhysicsPhase (waves)Markov processFOS: Physical sciencesQuantum entanglement03.65.Ud Entanglement and quantum nonlocality (e.g. EPR paradox Bell's inequalities GHZ states etc.)Condensed Matter PhysicsAtomic and Molecular Physics and OpticsSettore FIS/03 - Fisica Della Materiasymbols.namesake02.50.Ga Markov processeQubit42.50.Dv Quantum state engineering and measurementsymbolsStatistical physicsExponential decayQuantum Physics (quant-ph)QuantumMathematical PhysicsLight field03.67.Lx Quantum computation architectures and implementations03.65.Ud Entanglement and quantum nonlocality (e.g. EPR paradox Bell's inequalities GHZ states etc.); 42.50.Dv Quantum state engineering and measurements; 03.67.Mn Entanglement measures witnesses and other characterizations; 02.50.Ga Markov processes; 03.67.Lx Quantum computation architectures and implementations
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Spin-1/2 geometric phase driven by decohering quantum fields

2003

We calculate the geometric phase of a spin-1/2 system driven by a one and two mode quantum field subject to decoherence. Using the quantum jump approach, we show that the corrections to the phase in the no-jump trajectory are different when considering an adiabatic and non-adiabatic evolution. We discuss the implications of our results from both the fundamental as well as quantum computational perspective.

PhysicsMarkov processeQuantum discordQuantum PhysicsQuantum dynamicsGeneral Physics and AstronomyQuantum simulatorFOS: Physical sciencesOpen quantum systemClassical mechanicsQuantum error correctionquantum fieldQuantum mechanicsQuantum processQuantum algorithmQuantum dissipationQuantum Physics (quant-ph)
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Persistent random walks, variable length Markov chains and piecewise deterministic Markov processes *

2013

A classical random walk $(S_t, t\in\mathbb{N})$ is defined by $S_t:=\displaystyle\sum_{n=0}^t X_n$, where $(X_n)$ are i.i.d. When the increments $(X_n)_{n\in\mathbb{N}}$ are a one-order Markov chain, a short memory is introduced in the dynamics of $(S_t)$. This so-called "persistent" random walk is nolonger Markovian and, under suitable conditions, the rescaled process converges towards the integrated telegraph noise (ITN) as the time-scale and space-scale parameters tend to zero (see Herrmann and Vallois, 2010; Tapiero-Vallois, Tapiero-Vallois2}). The ITN process is effectively non-Markovian too. The aim is to consider persistent random walks $(S_t)$ whose increments are Markov chains with…

[MATH.MATH-PR] Mathematics [math]/Probability [math.PR]Variable length Markov chainProbability (math.PR)Semi Markov processesIntegrated telegraph noise[MATH.MATH-PR]Mathematics [math]/Probability [math.PR]Mathematics::ProbabilitySimple and double infinite combs.Variable memoryFOS: Mathematics[ MATH.MATH-PR ] Mathematics [math]/Probability [math.PR]Mathematics - ProbabilityPersistent random walkSimple and double infinite combsPiecewise Deterministic Markov Processes
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Modeling long-range memory with stationary Markovian processes

2009

In this paper we give explicit examples of power-law correlated stationary Markovian processes y(t) where the stationary pdf shows tails which are gaussian or exponential. These processes are obtained by simply performing a coordinate transformation of a specific power-law correlated additive process x(t), already known in the literature, whose pdf shows power-law tails 1/x^a. We give analytical and numerical evidence that although the new processes (i) are Markovian and (ii) have gaussian or exponential tails their autocorrelation function still shows a power-law decay =1/T^b where b grows with a with a law which is compatible with b=a/2-c, where c is a numerical constant. When a<2(1+c) th…

correlation methodMarkov processeMathematical optimizationStationary distributionStatistical Mechanics (cond-mat.stat-mech)LogarithmStochastic processdiffusionAutocorrelationFOS: Physical sciencesProbability density functionContext (language use)White noiseExponential functionStatistical physicswhite noiseCondensed Matter - Statistical MechanicsMathematics
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Robust delay-dependent H∞ control of uncertain time-delay systems with mixed neutral, discrete, and distributed time-delays and Markovian switching p…

2011

Author's version of an article published in the journal: IEEE Transactions on Circuits and Systems I: Regular Papers. Also available from the publisher at: http://dx.doi.org/10.1109/tcsi.2011.2106090 The problem of robust mode-dependent delayed state feedback H ∞ control is investigated for a class of uncertain time-delay systems with Markovian switching parameters and mixed discrete, neutral, and distributed delays. Based on the LyapunovKrasovskii functional theory, new required sufficient conditions are established in terms of delay-dependent linear matrix inequalities for the stochastic stability and stabilization of the considered system using some free matrices. The desired control is …

delay systems H∞ control linear matrix inequalities Markov processes uncertain systems delay-dependent delayed state feedback distributed delays Lyapunov-Krasovskii functionals Markovian switching numerical example Stochastic stability and stabilization sufficient conditions uncertain time-delay system control system stability convex optimization delay control systems stabilization state feedback switching systems time delay uncertainty analysis discrete time control systemsVDP::Technology: 500::Mechanical engineering: 570VDP::Mathematics and natural science: 400::Mathematics: 410
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Modeling TDS data and segmenting consumers thanks to a mixture of semi-Markov processes

2018

International audience

semi-markov processesmixture model[SDV.AEN] Life Sciences [q-bio]/Food and NutritionTemporal Dominance of Sensations[SDV.AEN]Life Sciences [q-bio]/Food and NutritionComputingMilieux_MISCELLANEOUS
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