Scalable optical neural network with nonlocally coupled coherent photonic processor
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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
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2026
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| _version_ | 1866910044999647232 |
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| author | Ren, Chun Tanomura, Ryota Ichinose, Kazuki Mizukami, Keigo Taguchi, Yoshitaka Fukui, Taichiro Nakano, Yoshiaki Tanemura, Takuo |
| author_facet | Ren, Chun Tanomura, Ryota Ichinose, Kazuki Mizukami, Keigo Taguchi, Yoshitaka Fukui, Taichiro Nakano, Yoshiaki Tanemura, Takuo |
| contents | Optical neural networks (ONNs) based on programmable photonic integrated circuits (PICs) offer a promising route toward low-latency and energy-efficient deep learning. However, conventional photonic implementations of matrix-vector multiplication (MVM) rely on locally connected architectures, such as Mach-Zehnder interferometer (MZI) meshes, whose number of active components scales quadratically with matrix size, severely limiting scalability. Here, we present a scalable ONN that overcomes this limitation by exploiting the intrinsically diffractive and nonlocal nature of coherent light inside a silicon photonic chip. Our approach employs cascaded stages of multiport directional couplers (MDCs) interleaved with compact phase-shifter arrays, enabling strong nonlocal coupling among multiple optical modes. We show that an MDC-based optical unitary converter (OUC) requires only $3N$ phase shifters to achieve uniform coverage over the $N$-dimensional complex unitary group, in stark contrast to the $O(N^2)$ scaling of conventional MZI meshes. Based on the singular value decomposition, we demonstrate that an $N\times N$ MVM can be realized using only $7N$ phase shifters, breaking the traditional $O(N^2)$ scaling barrier. We experimentally implement a 32-input silicon photonic MVM chip with a tenfold reduction in active components and validate its performance on various classification tasks. Our results establish a practical pathway toward large-scale, energy-efficient, and reconfigurable photonic neural networks. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2603_07174 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Scalable optical neural network with nonlocally coupled coherent photonic processor Ren, Chun Tanomura, Ryota Ichinose, Kazuki Mizukami, Keigo Taguchi, Yoshitaka Fukui, Taichiro Nakano, Yoshiaki Tanemura, Takuo Optics Optical neural networks (ONNs) based on programmable photonic integrated circuits (PICs) offer a promising route toward low-latency and energy-efficient deep learning. However, conventional photonic implementations of matrix-vector multiplication (MVM) rely on locally connected architectures, such as Mach-Zehnder interferometer (MZI) meshes, whose number of active components scales quadratically with matrix size, severely limiting scalability. Here, we present a scalable ONN that overcomes this limitation by exploiting the intrinsically diffractive and nonlocal nature of coherent light inside a silicon photonic chip. Our approach employs cascaded stages of multiport directional couplers (MDCs) interleaved with compact phase-shifter arrays, enabling strong nonlocal coupling among multiple optical modes. We show that an MDC-based optical unitary converter (OUC) requires only $3N$ phase shifters to achieve uniform coverage over the $N$-dimensional complex unitary group, in stark contrast to the $O(N^2)$ scaling of conventional MZI meshes. Based on the singular value decomposition, we demonstrate that an $N\times N$ MVM can be realized using only $7N$ phase shifters, breaking the traditional $O(N^2)$ scaling barrier. We experimentally implement a 32-input silicon photonic MVM chip with a tenfold reduction in active components and validate its performance on various classification tasks. Our results establish a practical pathway toward large-scale, energy-efficient, and reconfigurable photonic neural networks. |
| title | Scalable optical neural network with nonlocally coupled coherent photonic processor |
| topic | Optics |
| url | https://arxiv.org/abs/2603.07174 |