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Dissipative phase transitions in quantum systems have been largely studied under the so-called Markovian approximation, where the environments to which the systems are coupled are memoryless. Here, we present a generalization of the…

Quantum Physics · Physics 2024-10-28 Baptiste Debecker , John Martin , François Damanet

The stochastic trajectories of molecules in living cells, as well as the dynamics in many other complex systems, often exhibit memory in their path over long periods of time. In addition, these systems can show dynamic heterogeneities due…

Statistical Mechanics · Physics 2024-07-10 Michał Balcerek , Agnieszka Wyłomańska , Krzysztof Burnecki , Ralf Metzler , Diego Krapf

A wide class of non-Markovian completely positive master equations can be formulated on the basis of quantum collisional models. In this phenomenological approach the dynamics of an open quantum system is modeled through an ensemble of…

Quantum Physics · Physics 2013-09-26 Adrian A. Budini

We study the non-Markovian random continuous processes described by the Mori-Zwanzig equation. As a starting point, we use the Markovian Gaussian Ornstein-Uhlenbeck process and introduce an integral memory term depending on the past of the…

Statistical Mechanics · Physics 2019-12-04 S. S. Melnyk , V. A. Yampol'skii , O. V. Usatenko

I present an approach to calculate the finite-frequency quantum noise in systems strongly coupled to structured reservoirs based on the Markovian embedding. This technique consists of mapping of the non-Markovian dynamics of the original…

Mesoscale and Nanoscale Physics · Physics 2020-12-04 Krzysztof Ptaszynski

An algorithm for non-stationary spatial modelling using multiple secondary variables is developed. It combines Geostatistics with Quantile Random Forests to give a new interpolation and stochastic simulation algorithm. This paper introduces…

Methodology · Statistics 2022-01-13 Colin Daly

We microscopically model the decoherence dynamics of entangled coherent states under the influence of vacuum fluctuation. We derive an exact master equation with time-dependent coefficients reflecting the memory effect of the environment,…

Quantum Physics · Physics 2009-06-02 Jun-Hong An , Mang Feng , Wei-Min Zhang

Stochastic models of biochemical reaction networks are widely used to capture intrinsic noise in cellular systems. The typical formulation of these models are based on Markov processes for which there is extensive research on efficient…

Molecular Networks · Quantitative Biology 2025-12-03 Thomas P. Steele , David J. Warne

Memory effects arise in many complex systems, from protein folding, to the spreading of epidemics and financial decisions. While so-called non-Markovian dynamics is common in larger systems with interacting components, observations in…

A numerical method of calculating the non-Markovian evolution of a driven atom radiating into a structured continuum is developed. The formal solution for the atomic reduced density matrix is written as a Markovian algorithm by introducing…

Quantum Physics · Physics 2009-11-06 M. W. Jack , J. J. Hope

Non-homogeneous chain environments (e.g. segmented ion traps) are investigated through an exact diagonalization approach. Different spectral densities, including band-gaps, can be engineered to separately assess memory effects. Environment…

Quantum Physics · Physics 2018-04-06 Ruggero Vasile , Fernando Galve , Roberta Zambrini

We define non-Markovian quantum dynamics as evolution in which the current state depends on all past states, and completely characterize its structure under the assumptions of complete positivity and non-signalling. The resulting…

Quantum Physics · Physics 2026-05-07 Serhii Kryhin , Vivishek Sudhir

We propose a system identification method, Non-Markovian Optimization-based Modeling for Approximate Dynamics with Spatially-homogeneous memory (NOMADS), for identifying linear dynamical systems from a set of multi-dimensional time-series…

Optimization and Control · Mathematics 2026-01-27 Ryoji Anzaki , Kazuhiro Sato

In this paper, we consider modeling missing dynamics with a nonparametric non-Markovian model, constructed using the theory of kernel embedding of conditional distributions on appropriate Reproducing Kernel Hilbert Spaces (RKHS), equipped…

Methodology · Statistics 2020-07-10 Shixiao W. Jiang , John Harlim

We study the nonlocal Stefan problem, where the phase transition is described by a nonlocal diffusion as well as the change of enthalpy functions. By using a stochastic optimization approach introduced for the local case, we construct…

Analysis of PDEs · Mathematics 2026-04-21 Raymond Chu , Inwon Kim , Young-Heon Kim , Kyeongsik Nam

Machine learning methods have proved to be useful for the recognition of patterns in statistical data. The measurement outcomes are intrinsically random in quantum physics, however, they do have a pattern when the measurements are performed…

Quantum Physics · Physics 2020-04-14 I. A. Luchnikov , S. V. Vintskevich , D. A. Grigoriev , S. N. Filippov

Koopman spectral analysis has attracted attention for nonlinear dynamical systems since we can analyze nonlinear dynamics with a linear regime by embedding data into a Koopman space by a nonlinear function. For the analysis, we need to find…

Machine Learning · Statistics 2021-02-10 Tomoharu Iwata , Yoshinobu Kawahara

Problems in the field of open quantum systems often involve an environment that strongly influences the dynamics of excited states. Here we present a numerical method to model optical spectra of non-Markovian open quantum systems. The…

Quantum Physics · Physics 2025-05-01 Roosmarijn de Wit , Jonathan Keeling , Brendon W. Lovett , Alex W. Chin

Non-Markovian dynamics arise when a system is coupled to a bath with finite correlation time, giving rise to memory effects that allow the bath to temporarily store and return excitations. However, how memory modifies irreversibility and…

Statistical Mechanics · Physics 2026-04-29 Ken Funo , Tan Van Vu , Keiji Saito

In order to approximate a continuous time stochastic process by discrete time Markov chains one has several options to embed the Markov chains into continuous time processes. On the one hand there is the Markov embedding, which uses…

Probability · Mathematics 2020-04-17 Björn Böttcher