Exact relaxation to Gibbs and non-equilibrium steady states in the quantum cellular automaton Rule 54

We study the out-of-equilibrium dynamics of the quantum cellular automaton Rule 54 using a time-channel approach. We exhibit a family of (non-equilibrium) product states for which we are able to describe exactly the full relaxation dynamics. We use this to prove that finite subsystems relax to a one-parameter family of Gibbs states. We also consider inhomogeneous quenches. Specifically, we show that when the two halves of the system are prepared in two different solvable states, finite subsystems at finite distance from the centre eventually relax to the non-equilibrium steady state (NESS) predicted by generalised hydrodynamics. To the best of our knowledge, this is the first exact description of the relaxation to a NESS in an interacting system and, therefore, the first independent confirmation of generalised hydrodynamics for an inhomogeneous quench.

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Transient coarsening and the motility of optically heated Janus colloids in a binary liquid mixture

Soft Matter ◽

10.1039/d0sm00964d ◽

2020 ◽

Vol 16 (36) ◽

pp. 8359-8371

Author(s):

Juan Ruben Gomez-Solano ◽

Sutapa Roy ◽

Takeaki Araki ◽

S. Dietrich ◽

Anna Maciołek

Keyword(s):

Steady State ◽

Liquid Mixture ◽

Binary Liquid Mixture ◽

Binary Solvent ◽

Janus Particle ◽

Equilibrium Dynamics ◽

Binary Liquid ◽

Non Equilibrium

We study experimentally and theoretically the non-equilibrium dynamics of a binary solvent around a gold-capped Janus particle, lasting from the very moment of switching illumination on until a steady state is reached.

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Temperature chaos is present in off-equilibrium spin-glass dynamics

Communications Physics ◽

10.1038/s42005-021-00565-9 ◽

2021 ◽

Vol 4 (1) ◽

Author(s):

Marco Baity-Jesi ◽

Enrico Calore ◽

Andrés Cruz ◽

Luis Antonio Fernandez ◽

José Miguel Gil-Narvion ◽

...

Keyword(s):

Spin Glass ◽

Dynamic Effect ◽

Large Degree ◽

Equilibrium Temperature ◽

Experimental Conditions ◽

Temperature Changes ◽

Equilibrium Dynamics ◽

Glass Dynamics ◽

Extreme Sensitivity ◽

Non Equilibrium

AbstractExperiments featuring non-equilibrium glassy dynamics under temperature changes still await interpretation. There is a widespread feeling that temperature chaos (an extreme sensitivity of the glass to temperature changes) should play a major role but, up to now, this phenomenon has been investigated solely under equilibrium conditions. In fact, the very existence of a chaotic effect in the non-equilibrium dynamics is yet to be established. In this article, we tackle this problem through a large simulation of the 3D Edwards-Anderson model, carried out on the Janus II supercomputer. We find a dynamic effect that closely parallels equilibrium temperature chaos. This dynamic temperature-chaos effect is spatially heterogeneous to a large degree and turns out to be controlled by the spin-glass coherence length ξ. Indeed, an emerging length-scale ξ* rules the crossover from weak (at ξ ≪ ξ*) to strong chaos (ξ ≫ ξ*). Extrapolations of ξ* to relevant experimental conditions are provided.

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Supradegeneracy and the Second Law of Thermodynamics

Journal of Non-Equilibrium Thermodynamics ◽

10.1515/jnet-2019-0051 ◽

2020 ◽

Vol 45 (2) ◽

pp. 121-132

Author(s):

Daniel P. Sheehan

Keyword(s):

Steady State ◽

Statistical Mechanics ◽

Population Inversion ◽

Laboratory Experiments ◽

Second Law Of Thermodynamics ◽

Second Law ◽

Boltzmann Factor ◽

Energy Currents ◽

Non Equilibrium

AbstractCanonical statistical mechanics hinges on two quantities, i. e., state degeneracy and the Boltzmann factor, the latter of which usually dominates thermodynamic behaviors. A recently identified phenomenon (supradegeneracy) reverses this order of dominance and predicts effects for equilibrium that are normally associated with non-equilibrium, including population inversion and steady-state particle and energy currents. This study examines two thermodynamic paradoxes that arise from supradegeneracy and proposes laboratory experiments by which they might be resolved.

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Epidemic growth and Griffiths effects on an emergent network of excited atoms

Nature Communications ◽

10.1038/s41467-020-20333-7 ◽

2021 ◽

Vol 12 (1) ◽

Author(s):

T. M. Wintermantel ◽

M. Buchhold ◽

S. Shevate ◽

M. Morgado ◽

Y. Wang ◽

...

Keyword(s):

Complex Systems ◽

Power Laws ◽

Excitation Density ◽

Griffiths Phase ◽

Many Body ◽

Excited Atoms ◽

Equilibrium Dynamics ◽

Many Body Systems ◽

Excitation Number ◽

Non Equilibrium

AbstractWhether it be physical, biological or social processes, complex systems exhibit dynamics that are exceedingly difficult to understand or predict from underlying principles. Here we report a striking correspondence between the excitation dynamics of a laser driven gas of Rydberg atoms and the spreading of diseases, which in turn opens up a controllable platform for studying non-equilibrium dynamics on complex networks. The competition between facilitated excitation and spontaneous decay results in sub-exponential growth of the excitation number, which is empirically observed in real epidemics. Based on this we develop a quantitative microscopic susceptible-infected-susceptible model which links the growth and final excitation density to the dynamics of an emergent heterogeneous network and rare active region effects associated to an extended Griffiths phase. This provides physical insights into the nature of non-equilibrium criticality in driven many-body systems and the mechanisms leading to non-universal power-laws in the dynamics of complex systems.

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Modules or Mean-Fields?

Entropy ◽

10.3390/e22050552 ◽

2020 ◽

Vol 22 (5) ◽

pp. 552 ◽

Cited By ~ 2

Author(s):

Thomas Parr ◽

Noor Sajid ◽

Karl J. Friston

Keyword(s):

Steady State ◽

Message Passing ◽

Mean Field Theory ◽

Functional Anatomy ◽

Mean Field ◽

State Density ◽

Stochastic Dynamical Systems ◽

Conditional Independency ◽

The Brain ◽

Non Equilibrium

The segregation of neural processing into distinct streams has been interpreted by some as evidence in favour of a modular view of brain function. This implies a set of specialised ‘modules’, each of which performs a specific kind of computation in isolation of other brain systems, before sharing the result of this operation with other modules. In light of a modern understanding of stochastic non-equilibrium systems, like the brain, a simpler and more parsimonious explanation presents itself. Formulating the evolution of a non-equilibrium steady state system in terms of its density dynamics reveals that such systems appear on average to perform a gradient ascent on their steady state density. If this steady state implies a sufficiently sparse conditional independency structure, this endorses a mean-field dynamical formulation. This decomposes the density over all states in a system into the product of marginal probabilities for those states. This factorisation lends the system a modular appearance, in the sense that we can interpret the dynamics of each factor independently. However, the argument here is that it is factorisation, as opposed to modularisation, that gives rise to the functional anatomy of the brain or, indeed, any sentient system. In the following, we briefly overview mean-field theory and its applications to stochastic dynamical systems. We then unpack the consequences of this factorisation through simple numerical simulations and highlight the implications for neuronal message passing and the computational architecture of sentience.

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