Quantum Light Detection with Enhanced Photonic Neural Network

TL;DR

This paper presents a hybrid quantum-classical detection method that enhances photonic neural networks for quantum light sensing, improving accuracy and robustness with small nonlinearities and minimal network size.

Contribution

It introduces a hybrid detection protocol combining quantum reservoirs with neural networks, enabling practical, high-fidelity quantum sensors on integrated photonic platforms.

Findings

Improved quantum state classification, tomography, and feature regression.

Effective with low nonlinearity-to-loss ratio ($U/b3 b7 0.02$).

Achieved high performance with only five network nodes.

Abstract

Advances in quantum technologies are accelerating the demand for optical quantum state sensors that combine high precision, versatility, and scalability within a unified hardware platform. Quantum reservoir computing offers a powerful route toward this goal by exploiting the nonlinear dynamics of quantum systems to process and interpret quantum information efficiently. Photonic neural networks are particularly well suited for such implementations, owing to their intrinsic sensitivity to photon-encoded quantum information. However, the practical realisation of photonic quantum reservoirs remains constrained by the inherently weak optical nonlinearities of available materials and the technological challenges of fabricating densely coupled quantum networks. To address these limitations, we introduce a hybrid quantum-classical detection protocol that integrates the advantages of quantum reservoirs with the adaptive learning capabilities of analogue neural networks. This synergistic architecture substantially enhances information-extraction accuracy and robustness, enabling low-cost performance improvements of quantum light sensors. Based on the proposed approach, we achieved significant improvements in quantum state classification, tomography, and feature regression, even for reservoirs with a relatively small nonlinearity-to-losses ratio $U/\gamma \approx 0.02$ in a network of only five nodes. By reducing reliance on material nonlinearity and reservoir size, the proposed approach facilitates the practical deployment of high-fidelity photonic quantum sensors on existing integrated platforms, paving the way toward chip-scale quantum processors and photonic sensing technologies.