Computing with the complex nonlinear dynamics of an optomechanical oscillator

TL;DR

This paper demonstrates how an optomechanical oscillator's nonlinear dynamics can be harnessed as a physical reservoir for computation, enabling nonlinear transformations and short-term memory with a single integrated device.

Contribution

It introduces a novel use of optomechanical oscillators as physical reservoirs for computation, showcasing their ability to perform complex tasks without external feedback.

Findings

Successfully reconstructed nonlinear functions using the device.

Predicted chaotic time series evolution accurately.

Performed spoken digit classification with high fidelity.

Abstract

An optomechanical oscillator undergoes a Hopf bifurcation that connects two dynamical regimes with different information-processing capabilities: thermal Brownian motion and coherent self-sustained oscillation. Below threshold, the oscillator occupies a stable fixed point around which thermal fluctuations drive stochastic Brownian motion - a regime dominated by linear response, with only short-lived memory and negligible usable nonlinearity. Above threshold, radiation pressure, free-carrier dynamics, and thermo-optic relaxation act together to sustain a stable limit cycle that simultaneously provides both nonlinear transformation and dynamical memory. Here we show that this coherent regime can be used as a physical reservoir for computation: by perturbing the phonon-lasing attractor, the cavity performs nonlinear input-output transformations and retains short-term memory without any external feedback mechanism. Using only a single chip-integrated device with 20 virtual nodes, we reconstruct nonlinear functions, predict the evolution of chaotic time series, and perform spoken digit classification on a two-digit sub-task. The mechanical resonance frequency sets the intrinsic dynamical timescale of the reservoir and therefore its processing speed; while the present device operates near 0.4 GHz, optomechanical and nanomechanical systems can be engineered to reach multi-GHz and sub-terahertz frequencies, directly translating into a scalable path toward ultrafast integrated physical computing.