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<p>Photonic neuromorphic computing has emerged as a promising approach toward energy-efficient artificial neural networks (ANN). Nanolasers, in particular, have become attractive candidates due to their ultra-low power consumption and intrinsic nonlinear characteristics. In this work, we propose a photonic neuromorphic computing architecture based on symmetry-protected robust zero modes at the center of the optical spectrum in coupled semiconductor nanolaser arrays. We experimentally demonstrate that even a small set of coupled nanolasers inherently provides non-convex classification capabilities, enabling it to solve non-trivial classification tasks. As a benchmark, we show that a 2&#xa0;×&#xa0;2 nanolaser array, acting as a hidden nonlinear layer with recurrent coupling is able to solve the XNOR logical gate. Our results further highlight the computation capabilities of such nanolaser array by showing robust classification performance even under challenging conditions, such as the classification of highly compressed handwritten digits with significantly overlapping feature boundaries. These findings suggest that symmetry or topologically protected modes in nanolaser arrays can leverage robust optical connections to tackle complex problems without the need of scaling up the number of neurons.</p>

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Photonic neuromorphic computing using symmetry-protected zero modes in coupled nanolaser arrays

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年10月16日

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  • Kaiwen Ji,
  • Giulio Tirabassi,
  • Cristina Masoller,
  • Li Ge,
  • Alejandro M. Yacomotti

摘要

Photonic neuromorphic computing has emerged as a promising approach toward energy-efficient artificial neural networks (ANN). Nanolasers, in particular, have become attractive candidates due to their ultra-low power consumption and intrinsic nonlinear characteristics. In this work, we propose a photonic neuromorphic computing architecture based on symmetry-protected robust zero modes at the center of the optical spectrum in coupled semiconductor nanolaser arrays. We experimentally demonstrate that even a small set of coupled nanolasers inherently provides non-convex classification capabilities, enabling it to solve non-trivial classification tasks. As a benchmark, we show that a 2 × 2 nanolaser array, acting as a hidden nonlinear layer with recurrent coupling is able to solve the XNOR logical gate. Our results further highlight the computation capabilities of such nanolaser array by showing robust classification performance even under challenging conditions, such as the classification of highly compressed handwritten digits with significantly overlapping feature boundaries. These findings suggest that symmetry or topologically protected modes in nanolaser arrays can leverage robust optical connections to tackle complex problems without the need of scaling up the number of neurons.