Photon Number-Resolving Quantum Reservoir Computing

TL;DR

This paper proposes a practical optical quantum reservoir computing platform using photon number-resolved detection, enabling high-dimensional quantum processing with reduced complexity and current technology.

Contribution

It introduces a fixed optical network for quantum reservoir computing that leverages photon number-resolved detection, simplifying implementation and advancing quantum machine learning.

Findings

Reduces complexity of input quantum states

Accesses high-dimensional Hilbert space

Feasible with current technology

Abstract

Neuromorphic processors improve the efficiency of machine learning algorithms through the implementation of physical artificial neurons to perform computations. However, whilst 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 optical network for photonic quantum reservoir computing that is enabled by photon number-resolved detection of the output states. This significantly reduces the required complexity of the input quantum states while still accessing a high-dimensional Hilbert space. The approach is implementable with currently available technology and lowers the barrier to entry to quantum machine learning.