NSF-BSF: Quantum Optical Machines Powered by Heat and Information from Ideas to Experiments on Performance Bounds Grant uri icon

abstract

  • The upsurge of interest in quantum thermodynamics (QTD) centers around the basic questions: How are the laws of conventional thermodynamics potentially changed by quantum effects? Does QTD feature uniquely advantageous quantum resources? These issues will be researched by theory and experiment with mutual feedback to grasp the fundamental operation principles and performance bounds of QTD machines, e.g. laser-based quantum heat engines, quantum photocells, and biomolecular motors. This broad, yet targeted, research will help advance future technologies towards fast heat removal and dissipation control in nano-size devices, as the miniaturization of a refrigerator is one of the most important technological challenges. The overarching aim of the project is to bridge thermodynamics and quantum optics, both conceptually and operationally, and initiate QTD machine technologies based on platforms comprised of lasers, hot atomic (Rb) vapor, detectors, and cavities. The insights gained will help resolve underlying issues such as deviations in the performance bounds of quantum machines compared to classical heat machines. The group will introduce a universal criterion for work and heat extraction depending on the non-passivity (ergotropy) of the system state and the bath, unaccounted for by the second law. This criterion will allow the team to explore (by theory and experiment) the work-information, work-heat, and heat-information tradeoffs achievable in quantum systems. Anticipated are conceptually novel designs and experimental implementations of heat- and information-powered lasers without inversion, optical parametric amplifiers, and refrigerators without coherence, yet with advantageous performance. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

date/time interval

  • 2020 - 2025