Ultimate limits of thermal pattern recognition

Quantum channel discrimination presents a fundamental task in quantum information theory, with critical applications reading, illumination, data readout, and more. The extension to multiple has seen recent focus characterize potential advantage associated quantum-enhanced discriminatory protocols. In this paper, we study thermal imaging as an environment localization task, which images are modeled ensembles of Gaussian phase insensitive channels identical transmissivity, pixels possess properties according background (cold) or target (warm) channels. Via the teleportation stretching adaptive protocols, derive ultimate limits on precision pattern classification abstract, binary image spaces, show that strategies may be used provide significant over known optimal classical strategies. environmental conditions necessary resources for obtained studied discussed. We then numerically investigate use statistical classifiers, where sensors conjunction machine-learning methods. Proving definitive low-loss regime, work motivates sources short-range detection techniques future technologies.