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

McNemar's Test

McNemar's Test

McNemar's Test is a nonparametric statistical test used to determine if there is a significant difference in proportions between two related groups when the outcome is binary (e.g., yes/no, success/failure). It is beneficial when we have paired data, such as pre-test/post-test designs, where the same subjects are measured under two different conditions. The test is named after the statistician Quinn McNemar, who introduced it in 1947. It is commonly used in situations where subjects are...

You might also read

Related Articles

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

Sort by

Same author

miR-217/Mafb Axis Involve in High Glucose-Induced β-TC-tet Cell Damage Via Regulating NF-κB Signaling Pathway.

Biochemical genetics·2020

Same author

Fasciclin-like arabinogalactan gene family in Nicotiana benthamiana: genome-wide identification, classification and expression in response to pathogens.

BMC plant biology·2020

Same author

Discovery of Two Novel Negeviruses in a Dungfly Collected from the Arctic.

Viruses·2020

Same author

The OsGSK2 Kinase Integrates Brassinosteroid and Jasmonic Acid Signaling by Interacting with OsJAZ4.

The Plant cell·2020

Same author

Interleukin (IL)-33: an orchestrator of immunity from host defence to tissue homeostasis.

Clinical & translational immunology·2020

Same author

Higher Iodine Concentration Enables Radiation Dose Reduction in Coronary CT Angiography.

Academic radiology·2020

Search research articles

Related Experiment Video

Updated: May 15, 2025

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images

Published on: April 21, 2023

Exploring quantum neural networks for binary classification on MNIST dataset: A swap test approach.

Kehan Chen¹, Jiaqi Liu¹, Fei Yan¹

¹School of Computer Science and Technology, Changchun University of Science and Technology, Changchun 130022, China.

Neural Networks : the Official Journal of the International Neural Network Society

|April 10, 2025

Summary

This study introduces a modularized Quantum Neural Network (mQNN) for binary image classification on the MNIST dataset. The mQNN utilizes quantum parallelism for efficient computation, demonstrating potential in quantum machine learning.

Keywords:

Binary classification Quantum activation function Quantum neural networks Swap test

More Related Videos

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

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

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

Published on: March 2, 2015

Related Experiment Videos

Last Updated: May 15, 2025

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images

Published on: April 21, 2023

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

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

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

Published on: March 2, 2015

Area of Science:

Quantum Computing
Machine Learning
Image Classification

Background:

Traditional neural networks face challenges with increasing data complexity.
Quantum computing offers novel approaches for computational efficiency.

Purpose of the Study:

To develop a modularized Quantum Neural Network (mQNN) for binary classification tasks.
To leverage quantum parallelism and quantum-encoded parameters for enhanced processing.
To adapt mQNN structure for optimal performance across diverse data scales.

Main Methods:

Utilizing quantum images and trainable quantum parameters in superposition states.
Implementing the swap test for efficient inner product calculations with constant complexity.
Integrating quantum activation functions to increase network expressivity.
Constructing flexible quantum modules for adaptable circuit design.
Validating quantum state evolution through mathematical derivations.
Simulating the mQNN on the Pennylane platform.

Main Results:

The mQNN demonstrated effectiveness in binary classification on the MNIST dataset.
Quantum parallelism enabled efficient inner product calculations.
Flexible modular design allowed for adaptation to input data.

Conclusions:

The proposed mQNN is a viable model for image classification tasks.
Quantum computing holds significant potential for advancing machine learning in image recognition.
The mQNN architecture offers a promising direction for future quantum machine learning research.