Quantum Light Detection with Enhanced Photonic Neural Network

• URL: http://arxiv.org/abs/2601.19721v1
• Date: Tue, 27 Jan 2026 15:44:39 GMT
• Title: Quantum Light Detection with Enhanced Photonic Neural Network
• Authors: Stanisław Świerczewski, Dogyun Ko, Amir Rahmani, Juan Camilo López Carreño, Wouter Verstraelen, Piotr Deuar, Barbara Piętka, Timothy C. H. Liew, Michał Matuszewski, Andrzej Opala,
• Abstract summary: We introduce a hybrid quantum-classical detection protocol that integrates the advantages of quantum reservoirs with the adaptive learning capabilities of analogue neural networks.<n>This synergistic architecture substantially enhances information-extraction accuracy and robustness, enabling low-cost performance improvements of quantum light sensors.
• Score: 0.3775008736256856
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 $U/γ\approx 0.02$ 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.

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