Optical Reservoir Computing with Structural Nonlinearity for Forecasting Chaotic Time Series
Abstract
Optical computing offers a promising route to high-speed, energy-efficient information processing. However, implementing nonlinear operations typically requires strong light-matter interactions. Here, we introduce structural nonlinearity into an optical reservoir computing architecture, using only linear components. A digital micromirror device acts as a reconfigurable scattering potential, while the propagation of light through a scattering medium provides the high-dimensional mode mixing. By enforcing a second interaction with the wavefront shaper, we obtain a quadratic mapping of the input within the reservoir. A single experimental apparatus simultaneously implements conventional and structurally nonlinear reservoir architectures, enabling a direct comparison on forecasting of the chaotic Mackey-Glass time series. We demonstrate that the structurally nonlinear reservoir improves short-term prediction accuracy and better preserves the long-term dynamics of the chaotic system, demonstrating the potential of structural nonlinearity for low-power optical computing.
Related
- Disentangling Memory and Nonlinearity in Time-Multiplexed Optical Reservoir Computing
- Joint Forecasting of Extreme Events through Dual-Stage Cascade Reservoir Computing
- Hardware-Aware Design and Layout Selection in Quantum Reservoir Computing for Insurance Claims Forecasting
- Frequency-Multiplexed Photonic Reservoir Computing with a Synchronously Pulse-Driven Optical Cavity
- Optically writable atomic vapor memory as a substrate for optical reservoir computing
BibTeX
@misc{venancio2026optical,
title = {{Optical Reservoir Computing with Structural Nonlinearity for Forecasting Chaotic Time Series}},
author = {Leandro R. Venâncio and Jonathan Dong and Mickael Mounaix and Jacopo Bertolotti},
howpublished = {arXiv},
year = {2026},
month = sep,
doi = {10.48550/arxiv.2609.02733},
eprint = {2609.02733},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2609.02733},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Optical Reservoir Computing with Structural Nonlinearity for Forecasting Chaotic Time Series
AU - Leandro R. Venâncio
AU - Jonathan Dong
AU - Mickael Mounaix
AU - Jacopo Bertolotti
PY - 2026
DA - 2026/09/02
PB - arXiv
DO - 10.48550/arxiv.2609.02733
UR - https://arxiv.org/abs/2609.02733
AB - Optical computing offers a promising route to high-speed, energy-efficient information processing. However, implementing nonlinear operations typically requires strong light-matter interactions. Here, we introduce structural nonlinearity into an optical reservoir computing architecture, using only linear components. A digital micromirror device acts as a reconfigurable scattering potential, while the propagation of light through a scattering medium provides the high-dimensional mode mixing. By enforcing a second interaction with the wavefront shaper, we obtain a quadratic mapping of the input within the reservoir. A single experimental apparatus simultaneously implements conventional and structurally nonlinear reservoir architectures, enabling a direct comparison on forecasting of the chaotic Mackey-Glass time series. We demonstrate that the structurally nonlinear reservoir improves short-term prediction accuracy and better preserves the long-term dynamics of the chaotic system, demonstrating the potential of structural nonlinearity for low-power optical computing.
ER -