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Interval Markov decision processes are a class of Markov models where the transition probabilities between the states belong to intervals. In this paper, we study the problem of efficient estimation of the optimal policies in Interval…

Systems and Control · Electrical Eng. & Systems 2023-09-19 Saber Jafarpour , Samuel Coogan

In this work we study how the non-Markovian character of the dynamics can affect the thermodynamic performance of a quantum thermal engine, by analysing the maximum power output of Carnot and Otto cycles departing from the quasi-static and…

Quantum Physics · Physics 2019-05-15 Paolo Abiuso , Vittorio Giovannetti

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 develop a qualitative theory of Markov Decision Processes (MDPs) and Partially Observable MDPs that can be used to model sequential decision making tasks when only qualitative information is available. Our approach is based upon an…

Artificial Intelligence · Computer Science 2013-01-07 Blai Bonet , Judea Pearl

A path information is defined in connection with different possible paths of irregular dynamic systems moving in its phase space between two points. On the basis of the assumption that the paths are physically differentiated by their…

Statistical Mechanics · Physics 2007-05-23 Qiuping A. Wang

Principled prediction of when and where links form in complex networks is a fundamental problem. We derive a closed-form non-Markovian expression for next-step connection probabilities that unifies latent hyperbolic geometry with long-range…

Physics and Society · Physics 2026-04-28 Fragkiskos Papadopoulos

Real-world reinforcement learning is often \emph{nonstationary}: rewards and dynamics drift, accelerate, oscillate, and trigger abrupt switches in the optimal action. Existing theory often represents nonstationarity with coarse-scale models…

Machine Learning · Computer Science 2026-01-30 Zuyuan Zhang , Mahdi Imani , Tian Lan

The nonincreasing feature of temporal quantum steering under a completely positive trace-preserving (CPTP) map, as proposed by Chen, et al. in Phys. Rev. Lett. 116, 020503 (2016), has been considered as a practical measure of…

Quantum Physics · Physics 2024-05-03 Yan Li , Xingli Li , Jiasen Jin

Stochastic search processes are ubiquitous in nature and are expected to become more efficient when equipped with a memory, where the searcher has been before. A natural realization of a search process with long-lasting memory is a…

Statistical Mechanics · Physics 2021-08-18 Hugues Meyer , Heiko Rieger

We study the random acceleration model, which is perhaps one of the simplest, yet nontrivial, non-Markov stochastic processes, and is key to many applications. For this non-Markov process, we present exact analytical results for the…

Statistical Mechanics · Physics 2019-09-04 Satya N. Majumdar , Alberto Rosso , Andrea Zoia

Superdense coding is a significant technique widely used in quantum information processing. Indeed, it consists of sending two bits of classical information using a single qubit, leading to faster and more efficient quantum communication.…

Quantum Physics · Physics 2024-08-21 Y. Aiache , S. Al-Kuwari , K. El Anouz , A. El Allati

Recently, a large class of quantum non-Markovian piecewise dynamics for an open quantum system obeying closed evolution equations has been introduced [B. Vacchini, Phys. Rev. Lett. 117, 230401 (2016)]. These dynamics have been defined in…

Quantum Physics · Physics 2018-01-17 Salvatore Lorenzo , Francesco Ciccarello , G. Massimo Palma , Bassano Vacchini

Due to the time scale problem, rare events are not accessible by straight forward molecular dynamics. The presence of multiple reaction channels complicates the problem even further. The feasibility of the standard free energy based methods…

Statistical Mechanics · Physics 2009-11-13 Titus S. van Erp

A popular approach to solving a decision process with non-Markovian rewards (NMRDP) is to exploit a compact representation of the reward function to automatically translate the NMRDP into an equivalent Markov decision process (MDP) amenable…

Artificial Intelligence · Computer Science 2013-01-07 Sylvie Thiebaux , Froduald Kabanza , John Slanley

In this paper we propose a new method for approximating the nonstationary moment dynamics of one dimensional Markovian birth-death processes. By expanding the transition probabilities of the Markov process in terms of Poisson-Charlier…

Numerical Analysis · Mathematics 2014-09-23 Stefan Engblom , Jamol Pender

An analysis of the network defined by the potential energy minima of multi-atomic systems and their connectivity via reaction pathways that go through transition states allows to understand important characteristics like thermodynamic,…

Materials Science · Physics 2016-08-03 Bastian Schaefer , Stefan Goedecker

We apply the recently proposed quantum probing protocols with an unknown system-probe coupling to probe the convex coefficients in mixtures of commuting states. By using two reference states instead of one as originally suggested, we are…

Quantum Physics · Physics 2022-09-13 Henri Lyyra , Olli Siltanen , Jyrki Piilo , Subhashish Banerjee , Tom Kuusela

In nuclear fusion and fission, fluctuation and dissipation arise due to the coupling of collective degrees of freedom with internal excitations. Close to the barrier, both quantum, statistical and non-Markovian effects are expected to be…

Nuclear Theory · Physics 2010-04-06 G. Hupin , D. Lacroix

Most biochemical reactions in living cells are open systems interacting with environment through chemostats to exchange both energy and materials. At a mesoscopic scale, the number of each species in those biochemical reactions can be…

Optimization and Control · Mathematics 2022-11-08 Yuan Gao , Jian-Guo Liu

We consider a model of interacting neurons where the membrane potentials of the neurons are described by a multidimensional piecewise deterministic Markov process (PDMP) with values in ${\mathbb R}^N, $ where $ N$ is the number of neurons…

Statistics Theory · Mathematics 2016-10-04 Pierre Hodara , Nathalie Krell , Eva Löcherbach