  The challenge of pattern recognition is to invoke a strategy that can
accurately extract features of a dataset and classify its samples. In realistic
scenarios this dataset may be a physical system from which we want to retrieve
information, such as in the readout of optical classical memories. The
theoretical and experimental development of quantum reading has demonstrated
that the readout of optical memories can be dramatically enhanced through the
use of quantum resources (namely entangled input-states) over that of the best
classical strategies. However, the practicality of this quantum advantage
hinges upon the scalability of quantum reading, and up to now its experimental
demonstration has been limited to individual cells. In this work, we
demonstrate for the first time quantum advantage in the multi-cell problem of
pattern recognition. Through experimental realizations of digits from the MNIST
handwritten digit dataset, and the application of advanced classical
post-processing, we report the use of entangled probe states and
photon-counting to achieve quantum advantage in classification error over that
achieved with classical resources, confirming that the advantage gained through
quantum sensors can be sustained throughout pattern recognition and complex
post-processing. This motivates future developments of quantum-enhanced pattern
recognition of bosonic-loss within complex domains.
