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

Difference from Background: Limit of Detection01:05

Difference from Background: Limit of Detection

6.7K
The limit of detection (LOD) is the smallest amount of analyte that can be distinguished from the background noise. The LOD value corresponds to the concentration at which the analyte signal is three times larger than the standard deviation of the blank signal. Below this value, the analyte signal cannot be differentiated from the background noise. It is calculated by dividing the calibration slope by 3 times the standard deviation of the blank signals.
The LOD indicates the presence or absence...
6.7K
Neural Circuits01:25

Neural Circuits

1.3K
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...
1.3K
Insensitive Nuclei Enhanced by Polarization Transfer (INEPT)01:15

Insensitive Nuclei Enhanced by Polarization Transfer (INEPT)

408
Insensitive Nuclei Enhanced by Polarization Transfer (INEPT) is an advanced Nuclear Magnetic Resonance (NMR) technique specifically designed to detect and enhance the signals of low-abundance nuclei, such as carbon-13 and nitrogen-15, in small molecules. The fundamental principle behind INEPT is the transfer of polarization from a more abundant and highly polarizable nucleus, typically hydrogen-1, to the low-abundance nucleus of interest. This process effectively boosts the NMR signal of the...
408

You might also read

Related Articles

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

Sort by
Same author

A preliminary study on the effects of deposition pressure on the quality and intensity of eccrine fingermarks on paper.

Science & justice : journal of the Forensic Science Society·2026
Same author

Effects of acute warm-up intensity and baseline fitness on running performance in adolescents.

Frontiers in physiology·2026
Same author

Transcriptomic profiling of an in vivo diffuse large B-cell lymphoma model reveals molecular programs underlying CNS dissemination.

Annals of hematology·2026
Same author

Performance of a quality control center supporting national antimicrobial resistance surveillance.

British journal of biomedical science·2026
Same author

JAK-STAT3-MYC axis defines pathogenic stem cell-like memory CD4<sup>+</sup> T cells in rheumatoid arthritis.

Clinical immunology (Orlando, Fla.)·2026
Same author

Noise exposure and dizziness in middle-aged adults: a nationally representative study.

Frontiers in neurology·2026

Related Experiment Video

Updated: Jul 24, 2025

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks
11:18

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

Published on: March 2, 2015

10.4K

Quantum Neural Network Based Distinguisher on SPECK-32/64.

Hyunji Kim1, Kyungbae Jang1, Sejin Lim1

  • 1Division of IT Convergence Engineering, Hansung University, Seoul 02876, Republic of Korea.

Sensors (Basel, Switzerland)
|July 8, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces the first quantum neural network distinguisher for the SPECK-32 block cipher, demonstrating its potential despite current quantum computing limitations. While not outperforming classical methods, it shows promise for future advancements in quantum cryptography.

Keywords:
SPECK-32/64differentialdistinguisherquantum neural network

More Related Videos

Author Spotlight: Deciphering Neural Circuit Formation from Two-Photon Microscopy and Single Neuron Imaging
06:18

Author Spotlight: Deciphering Neural Circuit Formation from Two-Photon Microscopy and Single Neuron Imaging

Published on: November 21, 2023

836
Author Spotlight: Modular Neuronal Networks for Analyzing Brain Functions
07:38

Author Spotlight: Modular Neuronal Networks for Analyzing Brain Functions

Published on: June 7, 2024

1.6K

Related Experiment Videos

Last Updated: Jul 24, 2025

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks
11:18

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

Published on: March 2, 2015

10.4K
Author Spotlight: Deciphering Neural Circuit Formation from Two-Photon Microscopy and Single Neuron Imaging
06:18

Author Spotlight: Deciphering Neural Circuit Formation from Two-Photon Microscopy and Single Neuron Imaging

Published on: November 21, 2023

836
Author Spotlight: Modular Neuronal Networks for Analyzing Brain Functions
07:38

Author Spotlight: Modular Neuronal Networks for Analyzing Brain Functions

Published on: June 7, 2024

1.6K

Area of Science:

  • Quantum Computing and Cryptography
  • Machine Learning in Cybersecurity

Background:

  • Lightweight block ciphers like SPECK-32 are crucial for securing Internet of Things (IoT) sensor data.
  • Deep learning has emerged as a powerful tool for analyzing differential characteristics of block ciphers.
  • The advancement of quantum computing necessitates exploring quantum machine learning for cryptographic analysis.

Purpose of the Study:

  • To propose and evaluate the first quantum neural network (QNN) based distinguisher for the SPECK-32 block cipher within the Noisy Intermediate-Scale Quantum (NISQ) era.
  • To analyze the performance of a QNN distinguisher under constrained quantum computing environments.
  • To investigate the impact of various QNN parameters on distinguisher performance.

Main Methods:

  • Development of a quantum neural distinguisher tailored for the SPECK-32 block cipher.
  • Experimental evaluation of the QNN distinguisher on a NISQ device, assessing its accuracy and operational range (up to 5 rounds).
  • Comparative analysis against a classical deep learning distinguisher.
  • In-depth analysis of factors influencing QNN performance, including embedding methods, qubit count, and quantum layers.

Main Results:

  • The quantum neural distinguisher successfully operated for up to 5 rounds of SPECK-32.
  • Achieved an accuracy of 0.53, demonstrating its capability as a distinguisher (accuracy > 0.51) despite limitations.
  • Classical neural distinguisher achieved higher accuracy (0.93) due to current quantum hardware constraints.
  • Identified key QNN parameters (embedding, qubit count, layers) affecting performance, highlighting the need for careful tuning beyond resource scaling.

Conclusions:

  • The proposed quantum neural distinguisher is a pioneering step in applying QNNs to lightweight block cipher cryptanalysis in the NISQ era.
  • Current quantum hardware limitations restrict performance compared to classical methods, but the distinguisher proves functional.
  • Future improvements in quantum resources and parameter optimization are essential for enhancing QNN-based cryptographic distinguishers.