Seminar by Dr. Himangshu Prabal Goswami, Assistant Professor, Department of Chemistry, University of Gauhati, on ""

28 Nov 2022
Seminar Room # 350, second floor annex

Speaker:              Dr. Himangshu Prabal Goswami
                      Assistant Professor, Department of Chemistry,
                      University of Gauhati, Guwahati 781014,
                      Assam India

Title:                “Learning coherences from nonequilibrium fluctuations
                      in quantum heat engines.”

Day and Date:         Monday, November 28, 2022

Time:                 4.00 pm.

Venue:                Room no. 350, Chemistry Department
                      Second floor, Annex

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Hosted by Prof. Rajarshi Chakrabarti & Prof. Srinivasan Ramakrishnan

Talk Title : "Learning coherences from nonequilibrium fluctuations in quantum heat engines."
Abstract
Traditional or classical thermal engines absorb heat to produce useful mechanical work. Quantum heat engines in addition to having traditional properties are also affected by coherences and nonequilibrium fluctuations that arise due to the interactions between a quantized system and the quantum properties of the thermal baths [1]. The dependence of higher order power fluctuations on the quantum coherences however is nontrivial owing to the lack of knowledge of the relationship between these quantities despite both being experimentally measurable [2]. Usually, for a designed quantum engine, the coherences are rendered fixed while measuring the fluctuations. The other system parameters like coupling and temperatures act as control knobs to obtain desired values of fluctuations [2]. But these change the coherences creating an unknown mapping between observed and desired values. In this talk, I shall introduce a popular quantum heat engine where coherences are known to optimize the power beyond classical values [1,2]. I shall also show how one theoretically evaluates the experimentally observed fluctuations using a full counting statistical approach. Further, I shall show how machine learning algorithms based on gradient boosting and artificial neural networks allow us to use the information obtained from the fluctuations to predict the coherence values as well as other quantum thermodynamic observables and thermodynamic uncertainty relations.