Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Neural Circuits

Neural Circuits

Neural circuits and neuronal pools are two of the main structures found in the nervous system. Neural circuits are networks of neurons that work together to carry out a specific task or process. They consist of interconnected neurons and glial cells, which provide structural and metabolic support.
Neuronal pools are collections of nerve cells with similar functions and interact through chemical and electrical signals. These pools include both interneurons (the central neural circuit nodes that...

Photoreceptors and Visual Pathways

Photoreceptors and Visual Pathways

At the molecular level, visual signals trigger transformations in photopigment molecules, resulting in changes in the photoreceptor cell's membrane potential. The photon's energy level is denoted by its wavelength, with each specific wavelength of visible light associated with a distinct color. The spectral range of visible light, classified as electromagnetic radiation, spans from 380 to 720 nm. Electromagnetic radiation wavelengths exceeding 720 nm fall under the infrared category,...

The Quantum-Mechanical Model of an Atom

The Quantum-Mechanical Model of an Atom

Shortly after de Broglie published his ideas that the electron in a hydrogen atom could be better thought of as being a circular standing wave instead of a particle moving in quantized circular orbits, Erwin Schrödinger extended de Broglie’s work by deriving what is now known as the Schrödinger equation. When Schrödinger applied his equation to hydrogen-like atoms, he was able to reproduce Bohr’s expression for the energy and, thus, the Rydberg formula governing hydrogen spectra.

Neural Regulation

Neural Regulation

Digestion begins with a cephalic phase that prepares the digestive system to receive food. When our brain processes visual or olfactory information about food, it triggers impulses in the cranial nerves innervating the salivary glands and stomach to prepare for food.

Neuronal Communication

Neuronal Communication

Neurons, the fundamental units of the brain and nervous system, communicate through complex electrochemical signals that underpin all cognitive and bodily functions. This communication is primarily facilitated by a process involving the generation and propagation of an action potential along the axon of the neuron. When the internal electrical charge of a neuron surpasses a certain threshold, an action potential is triggered. This rapid change in voltage travels swiftly along the axon to the...

Hybridization of Atomic Orbitals II

Hybridization of Atomic Orbitals II

sp3d and sp3d 2 Hybridization

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Reinforcement learning control of quantum error correction.

Nature·2026

Same author

Optical metasurfaces for general vision processing on the edge.

Nature·2026

Same author

Author Correction: Programmable 200 GOPS Hopfield-inspired photonic Ising machine.

Nature·2026

Same author

Compact, reconfigurable, and scalable photonic neurons by modulation-and-weighting microring resonators.

eLight·2026

Same author

Neuromorphic photonic computing with an electro-optic analog memory.

Nature communications·2026

Same author

Online training and pruning of multi-wavelength photonic neural networks.

Nanophotonics (Berlin, Germany)·2025

Search research articles

Related Experiment Video

Updated: Jan 9, 2026

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Published on: September 8, 2023

Hybrid quantum-classical photonic neural networks.

Tristan Austin¹, Simon Bilodeau², Andrew Hayman¹

¹Centre for Nanophotonics, Department of Physics, Engineering Physics, and Astronomy, Queen's University, Ontario, Canada.

Npj Unconventional Computing

|December 4, 2025

Summary

This summary is machine-generated.

Hybrid quantum-classical networks combine classical and quantum circuits to boost artificial intelligence (AI) performance. These brain-inspired photonic systems achieve higher accuracy without increasing hardware size.

Keywords:

Computer science Integrated optics Quantum information

More Related Videos

Quantum State Engineering of Light with Continuous-wave Optical Parametric Oscillators

Quantum State Engineering of Light with Continuous-wave Optical Parametric Oscillators

Published on: May 30, 2014

A Photonic System for Generating Unconditional Polarization-Entangled Photons Based on Multiple Quantum Interference

A Photonic System for Generating Unconditional Polarization-Entangled Photons Based on Multiple Quantum Interference

Published on: September 5, 2019

Related Experiment Videos

Last Updated: Jan 9, 2026

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Published on: September 8, 2023

Quantum State Engineering of Light with Continuous-wave Optical Parametric Oscillators

Quantum State Engineering of Light with Continuous-wave Optical Parametric Oscillators

Published on: May 30, 2014

A Photonic System for Generating Unconditional Polarization-Entangled Photons Based on Multiple Quantum Interference

A Photonic System for Generating Unconditional Polarization-Entangled Photons Based on Multiple Quantum Interference

Published on: September 5, 2019

Area of Science:

Neuromorphic photonics
Quantum computing
Artificial intelligence

Background:

Neuromorphic photonics offers high-speed, energy-efficient AI solutions but faces hardware size limitations.
Scalable photonic neural networks can be achieved through advances in quantum hardware and trainable quantum circuits.

Purpose of the Study:

To investigate the potential of hybrid quantum-classical networks for enhancing neuromorphic photonic systems.
To demonstrate improved trainability and accuracy in AI tasks using these hybrid architectures.

Main Methods:

Combining classical network layers with trainable continuous variable quantum circuits.
Evaluating hybrid network performance on a classification task at state-of-the-art bit precisions.

Main Results:

Hybrid networks demonstrate improved trainability and accuracy compared to purely classical networks.
These hybrid systems achieve performance comparable to classical networks twice their size.
Performance benefits are maintained across different bit precisions for classical and quantum hardware.

Conclusions:

Hybrid quantum-classical networks offer a scalable approach to enhance the computational capacity of integrated photonic neural networks.
This approach overcomes size constraints in current neuromorphic photonic hardware.
A roadmap for implementing these hybrid architectures is presented.