Real-time calibration of coherent-state receivers: Learning by trial and error
M. Bilkis, Matteo Rosati, Rojas Yepes, John Calsamiglia
Abstract
This paper casts the discrimination of two coherent states of light as a reinforcement learning problem, in which an agent has to choose among a large number of configurations of a receiver, composed of simple linear optics elements, on/off photodetectors and feedback.
Topics & Concepts
Realization (probability)Reinforcement learningComputer scienceSet (abstract data type)PhotodetectorGaussianChannel (broadcasting)State (computer science)Energy (signal processing)State spaceKey (lock)AlgorithmControl theory (sociology)Electronic engineeringArtificial intelligenceTelecommunicationsMathematicsControl (management)PhysicsQuantum mechanicsEngineeringStatisticsComputer securityProgramming languageQuantum Information and CryptographyQuantum Computing Algorithms and ArchitectureQuantum Mechanics and Applications