[2509.01784] Modeling and benchmarking quantum optical neurons for efficient neural computation

Physics > Optics arXiv:2509.01784 (physics) [Submitted on 1 Sep 2025 (v1), last revised 20 Mar 2026 (this version, v2)] Title:Modeling and benchmarking quantum optical neurons for efficient neural computation Authors:Andrea Andrisani, Gennaro Vessio, Fabrizio Sgobba, Francesco Di Lena, Luigi Amato Santamaria, Giovanna Castellano View a PDF of the paper titled Modeling and benchmarking quantum optical neurons for efficient neural computation, by Andrea Andrisani and 5 other authors View PDF HTML (experimental) Abstract:Quantum optical neurons (QONs) are emerging as promising computational units that leverage photonic interference to perform neural operations in an energy-efficient and physically grounded manner. Building on recent theoretical proposals, we introduce a family of QON architectures based on Hong-Ou-Mandel (HOM) and Mach-Zehnder (MZ) interferometers, incorporating different photon modulation strategies -- phase, amplitude, and intensity. These physical setups yield distinct pre-activation functions, which we implement as fully differentiable software modules. We evaluate these QONs both in isolation and as building blocks of multilayer networks, training them on binary and multiclass image classification tasks using the MNIST and FashionMNIST datasets. Each experiment is repeated over five independent runs and assessed under both ideal and non-ideal conditions to measure accuracy, convergence, and robustness. Across settings, MZ-based neurons exhibit consistent...