Nonequilibrium fluctuations of a driven quantum heat engine via machine learning

Giri, Sajal Kumar; Goswami, Himangshu Prabal

doi:10.1103/physreve.99.022104

Cited by 10 publications

(7 citation statements)

References 57 publications

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“…This model has been studied in several previous works 27,[38][39][40][41] . The theoretical framework has already been developed and discussed before 27,37 and we refer to the appendix for necessary details. The engine operates in such a way that two thermal baths at temperatures T h (t) and T c (t), are adiabatically driven externally.…”

Section: Amplitude Modulated Driven Quantum Heat Enginementioning

confidence: 99%

“…Note that, the geometric contributions get explicitly added to the engine's thermodynamic properties due to the periodic driving of the reservoir temperatures. It is finite only when the driving protocols are phase different (which is introduced as a phase difference φ) 37 . Although we can observe driven dynamics when φ = 0, geometric contributions change the driven dynamics if and only if φ = 0.…”

Section: Amplitude Modulated Driven Quantum Heat Enginementioning

confidence: 99%

“…The net photon flux exchanged between the engine and cavity is a fluctuating quantity. Both the flux (j) and the noise or fluctuations (n) in photon exchange are measurable quantities and are composed of additive dynamic (subscript d) and geometric parts (subscript g) given by 37 ,…”

Section: A Flux and Noisementioning

confidence: 99%

“…We have also showed how other thermodynamic quantities of a popular QHE model such as flux, noise, efficiency, power etc. are influenced by such drivings 27,37 . Notably, we showed the universal linear slope of 1/2 in the standard efficiency at maximum power (EMP) as a function of Carnot efficiency (η c ) no longer holds when there is a finite phase difference between the two continuous driving protocols 27 .…”

Section: Introductionmentioning

confidence: 99%

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Controlling thermodynamics of a quantum heat engine with modulated amplitude drivings

Giri,

Goswami

2022

Preprint

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External driving of bath temperatures with a phase difference of a nonequilibrium quantum engine leads to the emergence of geometric effects on the thermodynamics. In this work, we modulate the amplitude of the external driving protocols by introducing envelope functions and study the role of geometric effects on the flux, noise and efficiency of a four-level driven quantum heat engine coupled with two thermal baths and a unimodal cavity. We observe that having a finite width of the modulation envelope introduces an additional control knob for studying the thermodynamics in the adiabatic limit. The optimization of the flux as well as the noise with respect to thermally induced quantum coherences becomes possible in presence of geometric effects, which is hitherto not possible with sinusoidal driving without an envelope. We also report the deviation of the slope and generation of an intercept in the standard expression for efficiency at maximum power as a function of Carnot efficiency in presence of geometric effects under the amplitude modulation. Further, a recently developed universal bound on the efficiency obtained from thermodynamic uncertainty relation is shown not to hold when a small width of the modulation envelope along with a large value of cavity temperature is maintained.

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Section: Amplitude Modulated Driven Quantum Heat Enginementioning

confidence: 99%

Section: Amplitude Modulated Driven Quantum Heat Enginementioning

confidence: 99%

Section: A Flux and Noisementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Controlling thermodynamics of a quantum heat engine with modulated amplitude drivings

Giri,

Goswami

2022

Preprint

Self Cite

View full text Add to dashboard Cite

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“…Nowadays, with the flourishing development of artificial intelligence (AI) [2], machine learning has become a new "smart" tool for analyzing experimental data in fundamental science, such as astrophysics [3,4], biological physics [5,6], condensed-matter physics [7][8][9][10][11], engineering mechanics [12], high-energy physics [13], statistical physics [14][15][16][17][18] and so on. It is the latest trend to discover fundamental laws of physics based on machine learning without prior experience about physics.…”

Section: Introductionmentioning

confidence: 99%

Exploring fundamental laws of classical mechanics via predicting the orbits of planets based on neural networks

Zhang,

Liu,

2021

Preprint

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Neural networks have provided powerful approaches to solve various scientific problems. Many of them are even difficult for human experts who are good at accessing the physical laws from experimental data. We investigate whether neural networks can assist us in exploring the fundamental laws of classical mechanics from data of planetary motion. Firstly, we predict the orbits of planets in the geocentric system using the gate recurrent unit, one of the common neural networks. We find that the precision of the prediction is obviously improved when the information of the Sun is included in the training set. This result implies that the Sun is particularly important in the geocentric system without any prior knowledge, which inspires us to gain Copernicus' heliocentric theory. Secondly, we turn to the heliocentric system and make successfully mutual predictions between the position and velocity of planets. We hold that the successful prediction is due to the existence of enough conserved quantities (such as conservations of mechanical energy and angular momentum) in the system. Our research provides a new way to explore the existence of conserved quantities in mechanics system based on neural networks.

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Cotunneling Effects in the Geometric Statistics of a Nonequilibrium Spin‐Resolved Junction

Sandilya,

Akhtar,

Sarmah

et al. 2024

Annalen der Physik

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In the nonequilibrium steady state of electronic transport across a spin‐resolved quantronic junction, the role of cotunneling on the emergent statistics under phase‐different adiabatic modulation of the reservoirs' chemical potentials is investigated. By explicitly identifying the sequential and inelastic cotunneling rates, the geometric or Pancharatnam–Berry contribution to the spin exchange flux between the spin system and the right reservoir is numerically evaluated. The relevant conditions wherein the sequential and cotunneling processes compete and selectively influence the total geometric flux upshot are identified. The Fock space coherences are found to suppress the cotunneling effects when the system reservoir couplings are comparable. The cotunneling contribution to the total geometric flux can be made comparable to the sequential contribution by creating a right‐sided asymmetrically stronger system‐reservoir coupling strength. Using a recently proposed geometric thermodynamic uncertainty relationship, the total rate of minimal entropy production is estimated. The geometric flux and the minimum entropy is found to be nonlinear as a function of the interaction energy of the junctions' spin orbitals.

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Nonequilibrium fluctuations of a driven quantum heat engine via machine learning

Cited by 10 publications

References 57 publications

Controlling thermodynamics of a quantum heat engine with modulated amplitude drivings

Controlling thermodynamics of a quantum heat engine with modulated amplitude drivings

Exploring fundamental laws of classical mechanics via predicting the orbits of planets based on neural networks

Cotunneling Effects in the Geometric Statistics of a Nonequilibrium Spin‐Resolved Junction

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