Variations on a Demonic Theme: Szilard's Other Engines
March 22, 2020 Β· Declared Dead Β· π Chaos
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Kyle J. Ray, James P. Crutchfield
arXiv ID
2003.09990
Category
cond-mat.stat-mech
Cross-listed
cs.IT,
nlin.CD
Citations
6
Venue
Chaos
Last Checked
3 months ago
Abstract
Szilard's now-famous single-molecule engine was only the first of three constructions he introduced in 1929 to resolve several paradoxes arising from Maxwell's demon. We analyze Szilard's remaining two demon models. We show that the second one, though a markedly different implementation employing a population of distinct molecular species and semi-permeable membranes, is informationally and thermodynamically equivalent to an ideal gas of the single-molecule engines. Since it is a gas of noninteracting particles one concludes, following Boyd and Crutchfield, that (i) it reduces to a chaotic dynamical system---called the Szilard Map, a composite of three piecewise linear maps that implement the thermodynamic transformations of measurement, control, and erasure; (ii) its transitory functioning as an engine that converts disorganized heat energy to work is governed by the Kolmogorov-Sinai entropy rate; (iii) the demon's minimum necessary "intelligence" for optimal functioning is given by the engine's statistical complexity, and (iv) its functioning saturates thermodynamic bounds and so it is a minimal, optimal implementation. We show that Szilard's third model is rather different and addresses the fundamental issue, raised by the first two, of measurement in and by thermodynamic systems and entropy generation. Taken together, Szilard's suite of constructions lays out a range of possible realizations of Maxwellian demons that anticipated by almost two decades Shannon's and Wiener's concept of information as surprise and cybernetics' notion of functional information. This, in turn, gives new insight into engineering implementations of novel nanoscale information engines that leverage microscopic fluctuations and into the diversity of thermodynamic mechanisms and intrinsic computation harnessed in physical, molecular, biochemical, and biological systems.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
π Similar Papers
In the same crypt β cond-mat.stat-mech
R.I.P.
π»
Ghosted
R.I.P.
π»
Ghosted
Unsupervised learning of phase transitions: from principal component analysis to variational autoencoders
π
π
Old Age
Unsupervised Generative Modeling Using Matrix Product States
R.I.P.
π»
Ghosted
Solving Statistical Mechanics Using Variational Autoregressive Networks
R.I.P.
π»
Ghosted
Learning Thermodynamics with Boltzmann Machines
R.I.P.
π»
Ghosted
Information Flows? A Critique of Transfer Entropies
Died the same way β π» Ghosted
R.I.P.
π»
Ghosted
Language Models are Few-Shot Learners
R.I.P.
π»
Ghosted
PyTorch: An Imperative Style, High-Performance Deep Learning Library
R.I.P.
π»
Ghosted
XGBoost: A Scalable Tree Boosting System
R.I.P.
π»
Ghosted