Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

You might also read

Related Articles

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

Sort by
Same author

Self-mixing in quantum cascade lasers for mid-infrared gas sensing: modeling, simulations, and experiments.

Optics express·2026
Same author

Per-Span Microwave-Frequency Fiber Interferometry for Amplified Transmission Links Employing High-Loss Loopbacks.

Sensors (Basel, Switzerland)·2026
Same author

Self-mixing detection of methane and carbon dioxide using mid-infrared quantum cascade lasers.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy·2026
Same author

Optimal performance of simple low-cost optical physical unclonable functions resilient to machine learning attacks : <sup>1</sup>Eulambia advanced technologies Ltd., Athens, Greece, <sup>2</sup>Department of informatics & Telecommunications, National and kapodistrian university of Athens, Athens, Greece.

Scientific reports·2025
Same author

Sparse polynomial chaos algorithm with a variance-adaptive design domain for the uncertainty quantification and optimization of grating structures.

Applied optics·2025
Same author

Reservoir computing based on transverse modes in a single optical waveguide.

Optics letters·2019
Same journal

Research on a Regional Availability Evaluation Model for Road-Area High-Entropy Energy Based on Synergy Factors.

Entropy (Basel, Switzerland)·2026
Same journal

Atmospheric Turbulence Channel Modeling and Performance Analysis of a CO-ZP-OFDM Coherent Optical Communication System for UAV Air-to-Ground Scenarios.

Entropy (Basel, Switzerland)·2026
Same journal

Information Geometry and Asymptotic Theory for SMML Estimators.

Entropy (Basel, Switzerland)·2026
Same journal

Correlation Entropy and Power-Law Kinetics.

Entropy (Basel, Switzerland)·2026
Same journal

Research on the Contagion of Systemic Financial Risk Under the Impact of Climate Risks-From the Perspective of Complex Networks and Machine Learning.

Entropy (Basel, Switzerland)·2026
Same journal

The Statistical-Mechanical Meaning of the Wave Function of Quantum Mechanics.

Entropy (Basel, Switzerland)·2026
See all related articles

Related Experiment Video

Updated: Jun 6, 2025

Real-Time Monitoring of Neurocritical Patients with Diffuse Optical Spectroscopies
07:12

Real-Time Monitoring of Neurocritical Patients with Diffuse Optical Spectroscopies

Published on: November 19, 2020

2.1K

Real-Time Diagnostics on a QKD Link via QBER Time-Series Analysis.

Georgios Maragkopoulos1, Aikaterini Mandilara1,2, Thomas Nikas1

  • 1Department of Informatics and Telecommunications, National and Kapodistrian University of Athens, Panepistimiopolis, 15784 Ilisia, Greece.

Entropy (Basel, Switzerland)
|November 27, 2024
PubMed
Summary
This summary is machine-generated.

We developed a machine learning method to identify quantum key distribution (QKD) link impairments in real-time. This approach uses quantum bit error rate (QBER) and secure key rate (SKR) data for broad applicability.

Keywords:
MLQKD

More Related Videos

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
05:30

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

Published on: September 8, 2023

487
Non-Invasive Monitoring of Microvascular Oxygenation and Reactive Hyperemia using Hybrid, Near-Infrared Diffuse Optical Spectroscopy for Critical Care
14:28

Non-Invasive Monitoring of Microvascular Oxygenation and Reactive Hyperemia using Hybrid, Near-Infrared Diffuse Optical Spectroscopy for Critical Care

Published on: May 10, 2024

1.5K

Related Experiment Videos

Last Updated: Jun 6, 2025

Real-Time Monitoring of Neurocritical Patients with Diffuse Optical Spectroscopies
07:12

Real-Time Monitoring of Neurocritical Patients with Diffuse Optical Spectroscopies

Published on: November 19, 2020

2.1K
Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
05:30

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

Published on: September 8, 2023

487
Non-Invasive Monitoring of Microvascular Oxygenation and Reactive Hyperemia using Hybrid, Near-Infrared Diffuse Optical Spectroscopy for Critical Care
14:28

Non-Invasive Monitoring of Microvascular Oxygenation and Reactive Hyperemia using Hybrid, Near-Infrared Diffuse Optical Spectroscopy for Critical Care

Published on: May 10, 2024

1.5K

Area of Science:

  • Quantum Information Science
  • Network Security
  • Machine Learning Applications

Background:

  • Integrating Quantum Key Distribution (QKD) systems into metro optical networks presents significant technical challenges.
  • Existing methods are insufficient for real-time identification of QKD link impairments within communication networks.

Purpose of the Study:

  • To devise a methodology for real-time identification of various impairments affecting QKD links in communication networks.
  • To develop a supervised machine learning model for this identification task.

Main Methods:

  • A supervised machine learning model was designed and trained.
  • The model utilizes time-series data of Quantum Bit Error Rate (QBER) and Secure Key Rate (SKR) as input.
  • The model's design ensures independence from specific QKD protocols or systems.

Main Results:

  • The developed methodology enables real-time identification of QKD link impairments.
  • The model successfully processes QBER and SKR data to determine link status.
  • The output provides critical information about the QKD link's working conditions.

Conclusions:

  • The proposed machine learning approach offers a versatile solution for monitoring QKD link health in metro optical networks.
  • This method enhances the reliability and manageability of QKD systems.
  • The insights gained are valuable for users and key management systems, improving overall network security.