Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Log inSign up
Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Blog
Job Board
Q3AS

© 2026 Aqora Quantum S.A.S.

TermsPrivacyLegal Notice

Studies in computational intelligence

Quantum Computing and GAN Aspect

Robin Singh Bhadoria, Vishrut Thakur, Tofigh Allahviranloo

Published Jan 1, 2025• 0 citations
Publisher
DOI
10.1007/978-3-031-82058-8_19

Related papers

Quantum-classical generative adversarial networks based computer vision solution for human and construction vehicle detection

Siva Rama Lingham N, R Prasanna Kumar

Detection of human and construction vehicle is an important step when it comes to automation of safety and productivity on construction sites. The technique of Computer Vision in its conventional forms has issues in cases where shadows, noise or large differences in illumination and occlusions occur. Although there are such challenges, the application of classical Generative Adversarial Networks (GANs) has promising solutions to such problems, but the training and optimization may take longer time and may not converge. The arrival of quantum computation allow for improvements on GAN characteristics through dimensional modeling and optimization at a high speed. Nevertheless, fully quantum models are still limited by hardware and can only be solved with the help of mixed-classifications. For construction vehicle detection, this research work suggests a combined Quantum-Classical Generative Adversarial Network (QC-GAN) approach. The connection component introduces quantum aspects into the procedure to capture elaborate features of the image data and enhance feature engineering and its generalisability. To enhance functioning of the QC-GAN, we incorporate the Leopard Seal Optimization (LSO) algorithm to function based on hunting attributes of leopard seal. The present LSO algorithm successfully navigates through the optimization landscape for better training stability and faster convergence. The introduced approach attains higher accuracy as 99%.

International Conference on Research Methodologies in Knowledge Management, Artificial Intelligence and Telecommunication Engineering (RMKMATE)•May 7, 2025•0 citations

QUANTUM COMPUTING AND ALGORITHMS

Mr. K. Suresh Babu Dr. T. P. Anish Dr. Cornelius K

•Mar 24, 2026•0 citations

Variational Quantum Circuits Enhanced Generative Adversarial Network

Runqiu Shu, Xusheng Xu, Man‐Hong Yung +1 more

Generative adversarial network (GAN) is one of the widely-adopted machine-learning frameworks for a wide range of applications such as generating high-quality images, video, and audio contents. However, training a GAN could become computationally expensive for large neural networks. In this work, we propose a hybrid quantum-classical architecture for improving GAN (denoted as QC-GAN). The performance was examed numerically by benchmarking with a classical GAN using MindSpore Quantum on the task of hand-written image generation. The generator of the QC-GAN consists of a quantum variational circuit together with a one-layer neural network, and the discriminator consists of a traditional neural network. Leveraging the entangling and expressive power of quantum circuits, our hybrid architecture achieved better performance (Frechet Inception Distance) than the classical GAN, with much fewer training parameters and number of iterations for convergence. We have also demonstrated the superiority of QC-GAN over an alternative quantum GAN, namely pathGAN, which could hardly generate 16$\times$16 or larger images. This work demonstrates the value of combining ideas from quantum computing with machine learning for both areas of Quantum-for-AI and AI-for-Quantum.

arXiv:2402.01791•Feb 2, 2024•4 citations

Quantum computing and algorithms

Author 1 - Mr. K. Suresh Babu Author 2 : Dr. T. P. Anish Author 3 : Dr. Cornelius K

•Dec 3, 2025•0 citations

Quantum Generative Adversarial Networks for Data Augmentation

Jincheng Zhang

This paper investigates the application of quantum computing to enhance the training of Generative Adversarial Networks (GANs) for data augmentation. Traditional GAN training is computationally intensive and often suffers from slow convergence and mode collapse. We propose leveraging quantum algorithms, specifically quantum gradient descent, to accelerate the training process and potentially improve the quality of synthetic data generated by GANs. The core claim is that quantum computing could significantly speed up and improve the training of GANs. The mechanism relies on using quantum algorithms for gradient calculations within the GAN architecture, leading to potentially faster convergence and the creation of more realistic synthetic data. This research explores the theoretical feasibility and potential benefits of this novel approach, paving the way for future investigations into hybrid quantum-classical GAN training methodologies.

•Aug 29, 2026•0 citations

Quantum Computing Overview

Osama Babakhel, Mujahid Tabassum, Tripti Sharma

Information systems engineering and management•Jan 1, 2025•0 citations
Detection of human and construction vehicle is an important step when it comes to automation of safety and productivity on construction sites. The technique of Computer Vision in its conventional forms has issues in cases where shadows, noise or large differences in illumination and occlusions occur. Although there are such challenges, the application of classical Generative Adversarial Networks (GANs) has promising solutions to such problems, but the training and optimization may take longer time and may not converge. The arrival of quantum computation allow for improvements on GAN characteristics through dimensional modeling and optimization at a high speed. Nevertheless, fully quantum models are still limited by hardware and can only be solved with the help of mixed-classifications. For construction vehicle detection, this research work suggests a combined Quantum-Classical Generative Adversarial Network (QC-GAN) approach. The connection component introduces quantum aspects into the procedure to capture elaborate features of the image data and enhance feature engineering and its generalisability. To enhance functioning of the QC-GAN, we incorporate the Leopard Seal Optimization (LSO) algorithm to function based on hunting attributes of leopard seal. The present LSO algorithm successfully navigates through the optimization landscape for better training stability and faster convergence. The introduced approach attains higher accuracy as 99%.
Generative adversarial network (GAN) is one of the widely-adopted machine-learning frameworks for a wide range of applications such as generating high-quality images, video, and audio contents. However, training a GAN could become computationally expensive for large neural networks. In this work, we propose a hybrid quantum-classical architecture for improving GAN (denoted as QC-GAN). The performance was examed numerically by benchmarking with a classical GAN using MindSpore Quantum on the task of hand-written image generation. The generator of the QC-GAN consists of a quantum variational circuit together with a one-layer neural network, and the discriminator consists of a traditional neural network. Leveraging the entangling and expressive power of quantum circuits, our hybrid architecture achieved better performance (Frechet Inception Distance) than the classical GAN, with much fewer training parameters and number of iterations for convergence. We have also demonstrated the superiority of QC-GAN over an alternative quantum GAN, namely pathGAN, which could hardly generate 16$\times$16 or larger images. This work demonstrates the value of combining ideas from quantum computing with machine learning for both areas of Quantum-for-AI and AI-for-Quantum.
This paper investigates the application of quantum computing to enhance the training of Generative Adversarial Networks (GANs) for data augmentation. Traditional GAN training is computationally intensive and often suffers from slow convergence and mode collapse. We propose leveraging quantum algorithms, specifically quantum gradient descent, to accelerate the training process and potentially improve the quality of synthetic data generated by GANs. The core claim is that quantum computing could significantly speed up and improve the training of GANs. The mechanism relies on using quantum algorithms for gradient calculations within the GAN architecture, leading to potentially faster convergence and the creation of more realistic synthetic data. This research explores the theoretical feasibility and potential benefits of this novel approach, paving the way for future investigations into hybrid quantum-classical GAN training methodologies.