Boson sampling powered image recognition

Quantum advantage has been demonstrated in a number of physical systems with boson sampling, a well-known restricted non-universal quantum computation model uniquely applicable to photonic systems, being one of the most significant examples. The boson sampling computational processors however has struggled to find real world practical applications they can be applied too. Here we show these processors can be used to perform various image recognition and classification tasks, even for modest sized systems. More specifically we show that these random interferometer-based boson sampling processors generate the complex dynamics necessary to power quantum reservoir computing.