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