Photon number-resolving quantum reservoir computing

Neuromorphic processors improve the efficiency of machine learning algorithms through the implementation of physical artificial neurons to perform computations. However, while efficient classical neuromorphic processors have been demonstrated in various forms, practical quantum neuromorphic platforms are still in the early stages of development. Here we propose a fixed, random optical network for photonic quantum machine learning in the specific form of a reservoir computer that is enabled by photon number-resolved detection of the output states. This provides access to a combinatorially scaling Hilbert space, while using significantly simpler quantum states than comparable quantum machine learning approaches. The approach is implementable with currently available technology and lowers the entry barrier for quantum machine learning.