Abstract
Mapping input signals to a high-dimensional space is a critical concept in various neuromorphic computing paradigms, including models such as reservoir computing (RC) and extreme learning machines (ELM). We propose using commercially available telecom devices and technologies developed for high-speed optical data transmission to implement these models through nonlinear mapping of optical signals into a high-dimensional space where linear processing can be applied. We manipulate the output feature dimension by applying temporal up-sampling (at the speed of commercially available telecom devices) of input signals and a well-established wave-division-multiplexing (WDM). Our up-sampling approach utilizes a trainable encoding mask, where each input symbol is replaced with a structured sequence of masked symbols, effectively increasing the representational capacity of the feature space. This gives remarkable flexibility in the dynamical phase masking of the input signal. We demonstrate this approach in the context of RC and ELM, employing readily available photonic devices, including a semiconductor optical amplifier and nonlinear Mach–Zehnder interferometer (MZI). We investigate how nonlinear mapping provided by these devices can be characterized in terms of the increased controlled separability and predictability of the output state.
Original language | English |
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Journal | Nanophotonics |
Early online date | 19 Jun 2025 |
DOIs | |
Publication status | E-pub ahead of print - 19 Jun 2025 |
Bibliographical note
Copyright © 2025 the author(s), published by De Gruyter. This work is licensed under the Creative Commons Attribution 4.0 International License.Data Access Statement
The datasets generated and/or analysed during the current study are available from the corresponding author upon reasonable request.Keywords
- nonlinear mapping
- reservoir computing
- extreme learning machine nonlinear optical loop mirror
- optical computing