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arXiv · 2507.19041

PGKET: A Photonic Gaussian Kernel Enhanced Transformer

Ren-Xin Zhao
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Abstract

Self-Attention Mechanisms (SAMs) enhance model performance by extracting key information but are inefficient when dealing with long sequences. To this end, a photonic Gaussian Kernel Enhanced Transformer (PGKET) is proposed, based on the Photonic Gaussian Kernel Self-Attention Mechanism (PGKSAM). The PGKSAM calculates the Photonic Gaussian Kernel Self-Attention Score (PGKSAS) using photon interferometry and superposition to process multiple inputs in parallel. Experimental results show that PGKET outperforms some state-of-the-art transformers in multi-classification tasks on MedMNIST v2 and CIFAR-10, and is expected to improve performance in complex tasks and accelerate the convergence of Photonic Computing (PC) and machine learning.

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Ren-Xin Zhao. 2025-07-25. PGKET: A Photonic Gaussian Kernel Enhanced Transformer. https://arxiv.org/abs/2507.19041

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