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

Elsevier eBooks

Revolutionizing network optimization: enhancing efficiency and performance through quantum computing and hybrid quantum–classical approaches

Bala Krushna Tripathy, T.R. Sooraj

Published Jan 1, 2026• 0 citations
Publisher
DOI
10.1016/b978-0-443-33136-7.00010-4

Related papers

Artificial Intelligence Techniques for Efficient Resource Management in 6G Communication Networks Using a Hybrid Quantum Duplet-Convolutional Neural Network Model: Trends and Challenges

Leocadia Nithisarn

The emergence of sixth-generation (6G) communication networks is expected to revolutionize wireless systems by enabling ultra-high data rates, ultra-low latency, and massive connectivity. Efficient resource management is a critical challenge due to the dynamic and heterogeneous nature of 6G environments. Traditional optimization techniques are insufficient to handle the complexity of such networks, leading to the adoption of artificial intelligence (AI)-driven approaches. This paper presents a comprehensive review of AI techniques for efficient resource management in 6G networks, focusing on hybrid quantum duplet-convolutional neural network (CNN) models. These models integrate quantum computing with classical deep learning to enhance computational efficiency and optimization capability. The study analyzes recent literature, covering deep learning, reinforcement learning, federated learning, and quantum machine learning approaches. A comparative analysis highlights the advantages of hybrid quantum-CNN models in terms of scalability, adaptability, and performance. The paper also discusses emerging trends such as AI-native network design, edge intelligence, and quantum-enhanced optimization. Furthermore, key challenges including computational complexity, energy consumption, and quantum hardware limitations are examined. The findings suggest that hybrid quantum deep learning models are promising for addressing resource management challenges in future 6G networks.

Multidisciplinary Journal of Research in Engineering and Technology•Aug 19, 2024•0 citations

Hybrid Quantum–Classical Optimization with Applications to Routing Problems

Peter Eder, Christian B. Mendl, Technische Universität München

•Jan 1, 2026•0 citations

Explore Hybrid Quantum-Classical Models for Enhanced Image Recognition

Madhav Sharma, Hukam Chand Saini, Samiksha Agarwal +2 more

The integration of quantum computing with classical device gaining knowledge of has the ability to revolutionize photograph popularity through leveraging the blessings of every paradigms. Hybrid quantum-classical fashions employ quantum computing functionality for highdimensional computation and superposition on the identical time as leveraging classical deep analyzing frameworks for function extraction and optimization. This paper explores the structure, advantages, worrying situations, and future recommendations of hybrid quantum-classical fashions in photograph recognition. We talk how quantum circuits may be embedded into convolution neural networks (CNNs) and have a look at average performance improvements as compared to classical models. Experimental results and simulations showcase the feasibility of hybrid strategies in improving photo category responsibilities

International Conference on Smart Computing and Communications (ICSCC)•Jul 3, 2025•0 citations

The Effectiveness of Utilizing a Hybrid Quantum Algorithm on Large Scale Network Routing

Bulgarian Journal of Physics•Dec 11, 2025•0 citations

Hybrid Quantum Neural Networks: Optimizing Computational Performance in Deep Learning

Shrabani Pramanick, Diganta Bhattacharyya

Hybrid quantum neural networks represent a novel approach to enhancing computational performance in deep learning tasks. By integrating quantum-inspired techniques with classical neural network architectures, these models aim to leverage the computational advantages of quantum computing while retaining the scalability and interpretability of classical methods. This paper explores the design and implementation of hybrid quantum neural networks, focusing on their ability to optimize computational efficiency in deep learning tasks. We present a comprehensive review of quantum-inspired optimization techniques and their application in neural network training. Through empirical evaluations and comparative studies, we demonstrate the effectiveness of hybrid quantum neural networks in improving training speed, convergence, and generalization performance. This work contributes to the advancement of efficient deep learning methodologies and lays the groundwork for further exploration of quantum-inspired approaches in machine learning. DOI - https://doi.org/10.65525/SVUP.9788199778009.2026.231-234

•Jan 1, 2026•0 citations

Quantum Optimization Techniques

Jesse Van Griensven Thé, Roydon Andrew Fraser, José R. Rosas-Bustos

Springer eBooks•Jan 1, 2025•0 citations
The emergence of sixth-generation (6G) communication networks is expected to revolutionize wireless systems by enabling ultra-high data rates, ultra-low latency, and massive connectivity. Efficient resource management is a critical challenge due to the dynamic and heterogeneous nature of 6G environments. Traditional optimization techniques are insufficient to handle the complexity of such networks, leading to the adoption of artificial intelligence (AI)-driven approaches. This paper presents a comprehensive review of AI techniques for efficient resource management in 6G networks, focusing on hybrid quantum duplet-convolutional neural network (CNN) models. These models integrate quantum computing with classical deep learning to enhance computational efficiency and optimization capability. The study analyzes recent literature, covering deep learning, reinforcement learning, federated learning, and quantum machine learning approaches. A comparative analysis highlights the advantages of hybrid quantum-CNN models in terms of scalability, adaptability, and performance. The paper also discusses emerging trends such as AI-native network design, edge intelligence, and quantum-enhanced optimization. Furthermore, key challenges including computational complexity, energy consumption, and quantum hardware limitations are examined. The findings suggest that hybrid quantum deep learning models are promising for addressing resource management challenges in future 6G networks.
The integration of quantum computing with classical device gaining knowledge of has the ability to revolutionize photograph popularity through leveraging the blessings of every paradigms. Hybrid quantum-classical fashions employ quantum computing functionality for highdimensional computation and superposition on the identical time as leveraging classical deep analyzing frameworks for function extraction and optimization. This paper explores the structure, advantages, worrying situations, and future recommendations of hybrid quantum-classical fashions in photograph recognition. We talk how quantum circuits may be embedded into convolution neural networks (CNNs) and have a look at average performance improvements as compared to classical models. Experimental results and simulations showcase the feasibility of hybrid strategies in improving photo category responsibilities
Hybrid quantum neural networks represent a novel approach to enhancing computational performance in deep learning tasks. By integrating quantum-inspired techniques with classical neural network architectures, these models aim to leverage the computational advantages of quantum computing while retaining the scalability and interpretability of classical methods. This paper explores the design and implementation of hybrid quantum neural networks, focusing on their ability to optimize computational efficiency in deep learning tasks. We present a comprehensive review of quantum-inspired optimization techniques and their application in neural network training. Through empirical evaluations and comparative studies, we demonstrate the effectiveness of hybrid quantum neural networks in improving training speed, convergence, and generalization performance. This work contributes to the advancement of efficient deep learning methodologies and lays the groundwork for further exploration of quantum-inspired approaches in machine learning. DOI - https://doi.org/10.65525/SVUP.9788199778009.2026.231-234