Enhanced Photonic Maxwell’s Demon with Correlated Baths PlatoBlockchain Data Intelligence. Vertical Search. Ai.

Enhanced Photonic Maxwell’s Demon with Correlated Baths

Guilherme L. Zanin1,2, Michael Antesberger1, Maxime J. Jacquet1,3, Paulo H. Souto Ribeiro2, Lee A. Rozema1, and Philip Walther1,4

1University of Vienna, Faculty of Physics, Vienna Center for Quantum Science and Technology (VCQ), Vienna, Austria
2Departamento de Física, Universidade Federal de Santa Catarina, Florianópolis, Santa Catarina 88040-900, Brazil
3Laboratoire Kastler Brossel, Sorbonne Université, CNRS, ENS-Université PSL, Collège de France, Paris 75005, France.
4Christian Doppler Laboratory for Photonic Quantum Computer, Faculty of Physics, University of Vienna, 1090 Vienna, Austria

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Abstract

Maxwell’s Demon is at the heart of the interrelation between quantum information processing and thermodynamics. In this thought experiment, a demon generates a temperature gradient between two thermal baths initially at equilibrium by gaining information at the single-particle level and applying classical feed-forward operations, allowing for the extraction of work. Here we implement a photonic version of Maxwell’s Demon with active feed-forward in a fibre-based system using ultrafast optical switches. We experimentally show that, if correlations exist between the two thermal baths, the Demon can generate a temperature difference over an order of magnitude larger than without correlations, and so extract more work. Our work demonstrates the great potential of photonic experiments – which provide a unique degree of control on the system – to access new regimes in quantum thermodynamics.

Maxwell’s Demon was initially imagined as an agent who can observe the microscopic properties of gas particles in a box and separate them based on their respective velocities, thus creating a temperature differential from which work may be extracted. In terms of information theory, the Demon’s action can be thought of as a feed-forward operation at the single-particle level—wherein a particle is measured, and the measurement result determines which operation the Demon will apply. Not only does this view put Maxwell’s Demon at the heart of the interrelation between thermodynamics and quantum information processing, but it allows us to create and use Maxwell Demons in experiments.
In our work, we empowered the Demon enabling it to generate a larger temperature imbalance per measurement, thus maximising the extractable work. Information theory shows that this may be done by providing the Demon with extra information. In our work, the source of the additional information is correlations between particles in the box. For example, imagine that for every fast particle on the right side of the box there is a corresponding fast particle moving on the left side moving in the same direction. In this case, measuring a particle on one side of the box also yields information about another particle on the other side of the box, making it easier to sort them.
Correlations are hard to engineer in most systems, like the gas particles in a box considered by Maxwell, but they are readily obtained in photonics, in which feed-forward operations are also possible. In our photonics experiment, we created a variety of thermal states of photons (which are the photonic analogue to thermally distributed gas particles), with and without correlations. The photonic Maxwell’s Demon creates a photon-number difference at the output (in analogy with the temperature imbalance of Maxwell’s thought experiment), i.e. an energy imbalance that could then be used to extract work, for example via radiation pressure. We showed that the photon number imbalance obtained with correlated states was ten times larger than the best imbalance obtained without correlations and could show that this is indeed linked to the information obtained by the Demon.
Our work shows that photonic experiments, with their high degree of flexibility and control, provide a new testing ground to realise and study quantum thermodynamics scenarios.

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