Holographic Transformers for Complex-Valued Signal Processing: Integrating Phase Interference into Self-Attention

• URL: http://arxiv.org/abs/2509.19331v2
• Date: Thu, 30 Oct 2025 03:42:04 GMT
• Title: Holographic Transformers for Complex-Valued Signal Processing: Integrating Phase Interference into Self-Attention
• Authors: Enhao Huang, Zhiyu Zhang, Tianxiang Xu, Chunshu Xia, Kaichun Hu, Yuchen Yang, Tongtong Pan, Dong Dong, Zhan Qin,
• Abstract summary: We introduce the Holographic Transformer, a physics-inspired architecture that incorporates wave interference principles into self-attention.<n>A dual-headed decoder simultaneously reconstructs the input and predicts task outputs, preventing phase collapse when losses prioritize magnitude over phase.<n>Experiments on PolSAR image classification and wireless channel prediction show strong performance, achieving high classification accuracy and F1 scores, low regression error, and increased robustness to phase perturbations.
• Score: 19.574464511943074
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Complex-valued signals encode both amplitude and phase, yet most deep models treat attention as real-valued correlation, overlooking interference effects. We introduce the Holographic Transformer, a physics-inspired architecture that incorporates wave interference principles into self-attention. Holographic attention modulates interactions by relative phase and coherently superimposes values, ensuring consistency between amplitude and phase. A dual-headed decoder simultaneously reconstructs the input and predicts task outputs, preventing phase collapse when losses prioritize magnitude over phase. We demonstrate that holographic attention implements a discrete interference operator and maintains phase consistency under linear mixing. Experiments on PolSAR image classification and wireless channel prediction show strong performance, achieving high classification accuracy and F1 scores, low regression error, and increased robustness to phase perturbations. These results highlight that enforcing physical consistency in attention leads to generalizable improvements in complex-valued learning and provides a unified, physics-based framework for coherent signal modeling. The code is available at https://github.com/EonHao/Holographic-Transformers.

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