Spatiotemporal computing for temporal nonlinear tasks using few-mode fiber and SOA nonlinearity
Abstract
Step-index few-mode fibers (FMFs) provide a compact passive platform for high-speed photonic information processing by exploiting modal dispersion to map temporal inputs into spatiotemporal representations with short-term memory. In previous studies, we have demonstrated linear classification tasks with such FMFs, such as ultrafast multibit header recognition. Here, we extend the computational capability of such architectures towards solving nonlinear tasks by introducing an optical nonlinearity at the output of the dispersive medium. The FMF output is processed through parallel direct and nonlinear branches, with the latter implemented using a nonlinear semiconductor optical amplifier (SOA). We experimentally evaluate the architecture at 28.5 Gb/s on two delayed XOR and higher-order parity tasks. We show that combining the direct and SOA-transformed representations reduces classification errors and broadens the range of operating conditions supporting low-error classification. In addition, by exploiting multiple temporal samples within each bit period as independent classifier features, we increase the dimensionality of the photonic representation and improve the computational performance.
Related
- Optical Reservoir Computing with Structural Nonlinearity for Forecasting Chaotic Time Series
- Disentangling Memory and Nonlinearity in Time-Multiplexed Optical Reservoir Computing
- QC-Stark: A Multi-Task Benchmark Revealing Capability Dissociations in LLMs Evaluated on Quantum Computing Tasks
- Nonlinear pluggable optics: Digital signal processing-free Intensity Modulated Direct Detection links using analog photonic Next Generation Reservoir Computing
- Temporal focusing enables distortion-resistant high-intensity spatiotemporal optical vortices
BibTeX
@misc{nazarudeen2026spatiotemporal,
title = {{Spatiotemporal computing for temporal nonlinear tasks using few-mode fiber and SOA nonlinearity}},
author = {Adhila Nazarudeen and Silvia Ortín and Apostolos Argyris},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2610.12173},
eprint = {2610.12173},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2610.12173},
}
RIS
TY - UNPB
TI - Spatiotemporal computing for temporal nonlinear tasks using few-mode fiber and SOA nonlinearity
AU - Adhila Nazarudeen
AU - Silvia Ortín
AU - Apostolos Argyris
PY - 2026
DA - 2026/10/08
PB - arXiv
DO - 10.48550/arxiv.2610.12173
UR - https://arxiv.org/abs/2610.12173
AB - Step-index few-mode fibers (FMFs) provide a compact passive platform for high-speed photonic information processing by exploiting modal dispersion to map temporal inputs into spatiotemporal representations with short-term memory. In previous studies, we have demonstrated linear classification tasks with such FMFs, such as ultrafast multibit header recognition. Here, we extend the computational capability of such architectures towards solving nonlinear tasks by introducing an optical nonlinearity at the output of the dispersive medium. The FMF output is processed through parallel direct and nonlinear branches, with the latter implemented using a nonlinear semiconductor optical amplifier (SOA). We experimentally evaluate the architecture at 28.5 Gb/s on two delayed XOR and higher-order parity tasks. We show that combining the direct and SOA-transformed representations reduces classification errors and broadens the range of operating conditions supporting low-error classification. In addition, by exploiting multiple temporal samples within each bit period as independent classifier features, we increase the dimensionality of the photonic representation and improve the computational performance.
ER -