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Related Experiment Video

Updated: Jun 29, 2026

Quantum State Engineering of Light with Continuous-wave Optical Parametric Oscillators
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In-situ training in programmable photonic frequency circuits.

Philip Rübeling1,2,3, Oleksandr V Marchukov1,2,3, Filipe F Bellotti4

  • 1Institute of Photonics (IOP), Leibniz University Hannover, Nienburger Str. 17, Hannover, Germany.

Nanophotonics (Berlin, Germany)
|August 13, 2025
PubMed
Summary

Researchers developed a novel optical artificial neural network (OANN) using frequency domain light. This photonic circuit achieves over 90% accuracy for multiclass classification, demonstrating a new platform for machine learning.

Keywords:
machine learningphotonic computingultrafast optics

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Area of Science:

  • Photonics
  • Machine Learning
  • Optical Computing

Background:

  • Optical artificial neural networks (OANNs) offer advantages like high speed and low energy consumption for machine learning.
  • Current OANN research often focuses on spatial or temporal light modes, but the frequency domain is gaining traction.
  • Existing frequency-domain OANNs include spectral multiplexing and nonlinear optical approaches.

Purpose of the Study:

  • To experimentally realize a programmable photonic frequency circuit for OANNs.
  • To implement in-situ training with optical weight control for a frequency-domain OANN.
  • To demonstrate the feasibility of multilayer OANNs operating in the optical frequency domain.

Main Methods:

  • Utilized fiber-optical components to construct a programmable photonic frequency circuit.
  • Encoded input data into the phases of frequency comb modes.
  • Employed programmable phase and amplitude manipulation of spectral modes for in-situ OANN training, bypassing digital models.

Main Results:

  • Achieved multiclass classification accuracies exceeding 90%, comparable to conventional machine learning methods.
  • Successfully demonstrated in-situ training of an OANN operating in the frequency domain.
  • Validated the proof-of-concept for a multilayer OANN in the frequency domain.

Conclusions:

  • The developed photonic frequency circuit enables feasible, in-situ trained OANNs.
  • This approach can be extended to scalable, integrated photonic platforms for ultrafast machine learning.
  • Potential applications include single-shot classification in spectroscopy and advanced AI acceleration.