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Related Concept Videos

Convolution: Math, Graphics, and Discrete Signals01:24

Convolution: Math, Graphics, and Discrete Signals

In any LTI (Linear Time-Invariant) system, the convolution of two signals is denoted using a convolution operator, assuming all initial conditions are zero. The convolution integral can be divided into two parts: the zero-input or natural response and the zero-state or forced response, with t0 indicating the initial time.
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Related Experiment Video

Updated: Jul 1, 2026

Generation and Coherent Control of Pulsed Quantum Frequency Combs
06:42

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Published on: June 8, 2018

Universal convolution from wave dynamics: photonic processing and encryption in synthetic dimension.

Xiaolong Su1, Weiwei Liu2, Ruiqian Cheng1

  • 1Wuhan National Laboratory for Optoelectronics and School of Physics, Huazhong University of Science and Technology, Wuhan, China.

Nature Communications
|June 29, 2026
PubMed
Summary
This summary is machine-generated.

Researchers demonstrate that wave dynamics in photonic lattices inherently perform convolution, enabling high-speed image processing and novel optical encryption. This breakthrough offers a new foundation for scalable photonic computing.

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Area of Science:

  • Photonics
  • Optical Computing
  • Signal Processing

Background:

  • Traditional convolution relies on complex hardware implementations.
  • Optical neural networks and signal processing require efficient convolution methods.

Purpose of the Study:

  • To demonstrate that wave dynamics in translation-symmetric lattices intrinsically perform convolution.
  • To develop a low-complexity convolutional architecture using programmable photonic synthetic lattices.
  • To explore applications beyond convolution acceleration, including simulating quantum dynamics and enabling optical encryption.

Main Methods:

  • Utilizing wave evolution in programmable photonic synthetic lattices.
  • Mapping mathematical convolution operations onto intrinsic wave dynamics.
  • Leveraging the dispersion relation to define the complex-valued kernel for convolution.

Main Results:

  • Achieved high-throughput, multifunctional capabilities at 13.5 tera-operations per second (TOPS) for image processing.
  • Demonstrated photonic simulation of irreversible diffusion and reversible unitary quantum dynamics.
  • Developed a convolution-driven optical encryption strategy using physics-based reversibility and phase information.

Conclusions:

  • Wave dynamics in translation-symmetric lattices intrinsically perform convolution, offering a new paradigm for photonic computing.
  • The developed architecture provides a scalable and highly integrated approach for multifunctional photonic processors.
  • This work unifies wave dynamics with computational principles, opening new avenues for optical information processing and security.