Universal convolution from wave dynamics: photonic processing and encryption in synthetic dimension
Fuente:
arXiv
Guardado en:
| Autores principales: | , , , , , , , , |
|---|---|
| Formato: | Preprint |
| Publicado: |
2025
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| _version_ | 1866915642353909760 |
|---|---|
| author | Su, Xiaolong Liu, Weiwei Cheng, Ruiqian Zhang, Haoru Guo, Xinyao Huang, He Qin, Chengzhi Lu, Peixiang Wang, Bing |
| author_facet | Su, Xiaolong Liu, Weiwei Cheng, Ruiqian Zhang, Haoru Guo, Xinyao Huang, He Qin, Chengzhi Lu, Peixiang Wang, Bing |
| contents | Convolution, a cornerstone of signal processing and optical neural networks, has traditionally been implemented by mapping mathematical operations onto complex hardware. Here, we overcome this challenge by revealing that wave dynamics in translation-symmetric lattices intrinsically performs convolution, with the dispersion relation uniquely defining the complex-valued kernel. Leveraging this universal principle, we develop a convolutional architecture of minimal complexity through wave evolution in programmable photonic synthetic lattices, delivering high-throughput, multifunctional capabilities at a rate of 13.5 tera-operations per second (TOPS) for image processing. Beyond convolution acceleration, the kernel's complex nature facilitates the photonic simulation of both irreversible diffusion and reversible unitary quantum dynamics under classical incoherent excitation. Capitalizing on the physics-based reversibility and undetectable phase information, we demonstrate a novel convolution-driven optical encryption strategy. This work establishes a unified framework for photonic computing by grounding convolution in wave dynamics, opening avenues toward scalable, multifunctional photonic processors with high integration potential. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_22426 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Universal convolution from wave dynamics: photonic processing and encryption in synthetic dimension Su, Xiaolong Liu, Weiwei Cheng, Ruiqian Zhang, Haoru Guo, Xinyao Huang, He Qin, Chengzhi Lu, Peixiang Wang, Bing Optics Convolution, a cornerstone of signal processing and optical neural networks, has traditionally been implemented by mapping mathematical operations onto complex hardware. Here, we overcome this challenge by revealing that wave dynamics in translation-symmetric lattices intrinsically performs convolution, with the dispersion relation uniquely defining the complex-valued kernel. Leveraging this universal principle, we develop a convolutional architecture of minimal complexity through wave evolution in programmable photonic synthetic lattices, delivering high-throughput, multifunctional capabilities at a rate of 13.5 tera-operations per second (TOPS) for image processing. Beyond convolution acceleration, the kernel's complex nature facilitates the photonic simulation of both irreversible diffusion and reversible unitary quantum dynamics under classical incoherent excitation. Capitalizing on the physics-based reversibility and undetectable phase information, we demonstrate a novel convolution-driven optical encryption strategy. This work establishes a unified framework for photonic computing by grounding convolution in wave dynamics, opening avenues toward scalable, multifunctional photonic processors with high integration potential. |
| title | Universal convolution from wave dynamics: photonic processing and encryption in synthetic dimension |
| topic | Optics |
| url | https://arxiv.org/abs/2511.22426 |