The power of being explicit: demystifying work, heat, and free energy in the physics of computation

Ouldridge, Thomas E.; Brittain, Rory A.; Wolde, Pieter Rein ten

doi:10.48550/arxiv.1812.09572

Cited by 2 publications

(3 citation statements)

References 66 publications

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“…By changing the concentration of the external bath of the species P , the chemical potentials µ can be controlled. Similarly to the analysis by Ouldridge and collaborators (71,72), we will imagine a set of different baths b each with a concentration of potential species [P b ] which can be freely disconnected and reconnected reaction volume. The purpose of this analysis is to highlight different costs of inference in certain extreme conditions which can act as benchmarks for future investigations.…”

Section: Thermodynamics Of Inference Via Clampingmentioning

confidence: 99%

“…[P ] The Reversible Case: Following the analysis by Ouldridge and collaborators (71), we consider a process in which the potentiated dbCRN begins in an equilibrium distribution π P 0 connected to a bath of potential species at concentration [P 0 ]. We now disconnect the potentiated dbCRN from this potential bath and instantaneously reconnect it to a new bath [P 1 ] then allow the potentiated dbCRN to equilibrate to a new equilibrium distribution π P 1 .…”

Section: Thermodynamics Of Inference Via Clampingmentioning

confidence: 99%

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Detailed Balanced Chemical Reaction Networks as Generalized Boltzmann Machines

Poole¹,

Ouldridge²,

Gopalkrishnan³

et al. 2022

Preprint

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Can a micron sized sack of interacting molecules understand, and adapt to a constantly-fluctuating environment? Cellular life provides an existence proof in the affirmative, but the principles that allow for life's existence are far from being proven. One challenge in engineering and understanding biochemical computation is the intrinsic noise due to chemical fluctuations. In this paper, we draw insights from machine learning theory, chemical reaction network theory, and statistical physics to show that the broad and biologically relevant class of detailed balanced chemical reaction networks is capable of representing and conditioning complex distributions. These results illustrate how a biochemical computer can use intrinsic chemical noise to perform complex computations. Furthermore, we use our explicit physical model to derive thermodynamic costs of inference.

show abstract

Section: Thermodynamics Of Inference Via Clampingmentioning

confidence: 99%

Section: Thermodynamics Of Inference Via Clampingmentioning

confidence: 99%

Detailed Balanced Chemical Reaction Networks as Generalized Boltzmann Machines

Poole¹,

Ouldridge²,

Gopalkrishnan³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…It has been discussed at great length in the literature that the costs associated with cyclically running a memory can be completely assigned to the erasing of that memory, by implementing a carefully designed protocol [4,31,[45][46][47][48][49]. This special protocol that contains a step in which the memory is reset to a certain state (i.e.…”

Section: Erasure Protocolsmentioning

confidence: 99%

Partially observable Szilárd engines

Still

Daimer

2022

New J. Phys.

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Almost a century ago, Leo Szilárd replaced Maxwell’s ‘demon’ by machinery. The resulting information engine concept laid the foundation for studying the physical nature of information. Szilárd reduced the demon’s role to mapping an observable onto a work extraction protocol, thereby creating and utilizing a meta-stable memory. While Szilárd demonstrated that this map can be implemented mechanistically, it was not chosen automatically by the demon, or ‘observer’, but rather given a priori. This choice implements the demon’s intelligence. In Szilárd’s original setup, the choice is trivial, but we show here that nontrivial data representations emerge for generalized, partially observable Szilárd engines. Partial observability is pervasive in real world systems with limited sensor types and information acquisition bandwidths. Generalized information engines may run work extraction at a higher temperature than memory formation, which enables the combined treatment of heat- and information engines. To date, Szilárd’s (fully observable) information engine still serves as a canonical example. Implications of partial observability are under-explored, despite their ubiquitous nature. We provide here the first physical characterization of observer memories that result in minimal engine dissipation. We introduce a new canonical model, simple yet physically rich: a minor change to Szilárd’s engine—inserting the divider at an angle—results in partially observable engines. We demonstrate how the demon’s intelligence can be automated. For each angle and for each temperature ratio, an optimal memory is found algorithmically, enabling the engine to run with minimal dissipation. While naive coarse graining is sufficient for the special case of full observability, in general, minimally dissipative observers use probabilistic memories. We propose a simple model for an implementation of these memories, and construct a nontrivial physical codebook. We characterize the performance of engines with minimally dissipative memories, and compare their quality to that of engines using an optimized coarse graining of the observable.

show abstract

The power of being explicit: demystifying work, heat, and free energy in the physics of computation

Cited by 2 publications

References 66 publications

Detailed Balanced Chemical Reaction Networks as Generalized Boltzmann Machines

Detailed Balanced Chemical Reaction Networks as Generalized Boltzmann Machines

Partially observable Szilárd engines

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