Optical free space extreme learning machine for the implementation of emergent complex systems
Abstract
Cellular automata conform a set of computational models which evolve with a reduced set of simple rules, yet still are able to show extremely complex emergent phenomena such as fractals and universal computation. Despite their apparent simplicity, they have shown great potential in simulating natural systems and solving challenging computational tasks such as classification and image generation. Instead of implementing cellular automata purely at the software level, it is desirable to design novel analog computing platforms that physically evolve following the automata's underlying rules, thereby reducing power requirements and latency. Here, we introduce an optical extreme learning machine for the simulation of a wide range of cellular automata. Our system operates in free space, and uses a spatial light modulator to encode the evolution rules of the system, while coherent wave propagation performs the corresponding computations. Our results demonstrate a simple, fully-programmable, cost and power efficient, and easy to build and align platform for the implementation of a wide range of complex computational systems such as elementary cellular automata, Conway's Game of Life, and two-dimensional Turing machines.
Related
- Nonlinear polarization effects in optical fiber-based extreme learning machine
- Photonic Time-Delayed Quantum Extreme Learning Machine
- Characterizing Privacy Risks of Quantum Machine Learning with Emergent Quantum-Native Access
- Plasmonic Lithography Optical Proximity Correction Method Based on Decomposed Machine Learning Fast Imaging Model
- Machine Learning Assisted Design of Complex and High Entropy Alloys by Hybrid HiPIMS/Pulsed-DC PVD Process for Low Carbon Energy Applications in Extreme Environments
BibTeX
@misc{moreno2026optical,
title = {{Optical free space extreme learning machine for the implementation of emergent complex systems}},
author = {Elena Moreno and Fernando Soldevila and Daniel Torrent},
howpublished = {arXiv},
year = {2026},
month = sep,
doi = {10.48550/arxiv.2609.00933},
eprint = {2609.00933},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2609.00933},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Optical free space extreme learning machine for the implementation of emergent complex systems
AU - Elena Moreno
AU - Fernando Soldevila
AU - Daniel Torrent
PY - 2026
DA - 2026/09/01
PB - arXiv
DO - 10.48550/arxiv.2609.00933
UR - https://arxiv.org/abs/2609.00933
AB - Cellular automata conform a set of computational models which evolve with a reduced set of simple rules, yet still are able to show extremely complex emergent phenomena such as fractals and universal computation. Despite their apparent simplicity, they have shown great potential in simulating natural systems and solving challenging computational tasks such as classification and image generation. Instead of implementing cellular automata purely at the software level, it is desirable to design novel analog computing platforms that physically evolve following the automata's underlying rules, thereby reducing power requirements and latency. Here, we introduce an optical extreme learning machine for the simulation of a wide range of cellular automata. Our system operates in free space, and uses a spatial light modulator to encode the evolution rules of the system, while coherent wave propagation performs the corresponding computations. Our results demonstrate a simple, fully-programmable, cost and power efficient, and easy to build and align platform for the implementation of a wide range of complex computational systems such as elementary cellular automata, Conway's Game of Life, and two-dimensional Turing machines.
ER -