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 systems, decision and control

Quantum-Enhanced Federated Learning Framework for Next-Generation AIoT Systems: Bridging Neuromorphic Computing and Environmental Intelligence

Viraj P. Tathavadekar, Nitin R. Mahankale

Published Oct 3, 2026• 0 citations
Publisher
DOI
10.1007/978-3-032-32770-3_18

Related papers

Neuromorphic-Quantum Hybrid Learning: The Next Evolution Beyond Deep Learning

Soham Khadse

As Moore’s Law approaches its physical limits and traditional deep learning architectures encounter fundamental scaling barriers, the convergence of neuromorphic computing, quantum information processing, and deep learning represents the most promising pathway toward next-generation artificial intelligence systems. Current deep learning approaches, while revolutionary, face critical limitations including exponential energy consumption, limited adaptability, and architectural constraints that prevent true cognitive capabilities. This paper introduces Neuromorphic-Quantum Hybrid Learning (NQHL), a revolutionary computational paradigm that synergistically combines brain-inspired neuromorphic circuits, quantum computational advantages, and deep learning methodologies to create the first truly cognitive artificial intelligence architecture. Our approach addresses four fundamental challenges in next-generation AI: energy-efficient computation through neuromorphic substrates, exponential processing advantages via quantum parallelism, adaptive learning through bio-inspired plasticity mechanisms, and scalable architectures that transcend von Neumann limitations. We develop novel techniques including Quantum Synaptic Networks (QSN), Bio-Inspired Quantum Circuits (BIQC), Hybrid Plasticity Algorithms (HPA), and Neuromorphic-Quantum Interface Protocols (NQIP) that collectively enable AI systems with unprecedented cognitive capabilities. Through comprehensive theoretical analysis and simulation studies, we demonstrate that NQHL achieves 1000x energy efficiency improvements while maintaining 95% accuracy across diverse cognitive tasks including pattern recognition, causal reasoning, and creative problem-solving. Our framework successfully bridges the gap between biological intelligence principles and quantum computational advantages, providing the foundational architecture for post-digital AI systems that can operate at the scale and efficiency required for artificial general intelligence.

Journal of Pioneering Artificial Intelligence Research•Oct 1, 2025•0 citations

Quantum federated learning: architectural elements and future directions

Siva Sai, Abhishek Sawaika, Prabhjot Singh +1 more

arXiv:2510.17642•Elsevier eBooks•Jan 1, 2026•4 citations

Quantum Federated Learning: Progress, Challenges, and Opportunities

Sanchita Saha, Arup Kumar Chattopadhyay, Amitava Nag

Algorithms for intelligent systems•Jan 1, 2025•1 citation

Agentic Hybrid Quantum–Neuromorphic AI Architecture With Explainable Reinforcement Learning for Next-Generation Intelligent Systems

Divyanshu Sinha, Udit Mamodiya, Indra Kishor +2 more

Artificial intelligence systems are shifting away to passive prediction systems into architectures that are supposed to rationalize and modify according to their needs as well as explain what they are doing. The current methods tend to maximize the precision at the expense of transparency and long-term flexibility, creating a gap in the systems with both accountable reasoning and learning processes. To solve this, we present an Agentic Hybrid Quantum -Neuromorphic AI system with an Explainable Reinforcement Learning core. It consists of a combination of quantum-assisted exploration, spiking neuromorphic adaptation, interpretable policy optimization to enable the system to adapt computational strategy depending on the dynamics of rewards, confidence, and environmental change. Experiments exhibit enhanced convergence speed, ability to maintain behavior in the case of sudden changes in the environment and a far greater enhancement in explanation coherence than traditional reinforcement learning agents and adaptive immersive systems.

Advances in computational intelligence and robotics book series•Mar 4, 2026•2 citations

Explainable Quantum Deep Learning Framework Integrating Vision Transformers and Federated Intelligence for Smart Healthcare Applications

Dr Ankam Raghavendra Rao

Modern healthcare systems increasingly rely on artificial intelligence, deep learning, and medical imaging technologies for accurate disease diagnosis and clinical decision-making. However, conventional AI systems still face challenges related to interpretability, computational complexity, centralized data dependency, and patient privacy. This paper presents an Explainable Quantum Deep Learning Framework integrating Vision Transformers, Federated Intelligence, and Explainable Artificial Intelligence techniques for smart healthcare applications. The proposed framework combines quantum-inspired optimization, multimodal medical data analysis, transformer-based feature extraction, and decentralized federated learning to improve diagnostic accuracy, scalability, privacy preservation, and interpretability. Explainability mechanisms including SHAP and Grad-CAM are incorporated to enhance physician trust and clinical transparency. Experimental evaluation demonstrates superior performance in biomedical image classification and disease prediction compared with conventional deep learning models, providing a scalable solution for next-generation intelligent healthcare systems.

JOURNAL OF ADVANCE AND FUTURE RESEARCH•May 1, 2026•0 citations

Quantum computing meets federated learning

Kaifeng Bu

Science China Physics Mechanics and Astronomy•Nov 8, 2021•4 citations
As Moore’s Law approaches its physical limits and traditional deep learning architectures encounter fundamental scaling barriers, the convergence of neuromorphic computing, quantum information processing, and deep learning represents the most promising pathway toward next-generation artificial intelligence systems. Current deep learning approaches, while revolutionary, face critical limitations including exponential energy consumption, limited adaptability, and architectural constraints that prevent true cognitive capabilities. This paper introduces Neuromorphic-Quantum Hybrid Learning (NQHL), a revolutionary computational paradigm that synergistically combines brain-inspired neuromorphic circuits, quantum computational advantages, and deep learning methodologies to create the first truly cognitive artificial intelligence architecture. Our approach addresses four fundamental challenges in next-generation AI: energy-efficient computation through neuromorphic substrates, exponential processing advantages via quantum parallelism, adaptive learning through bio-inspired plasticity mechanisms, and scalable architectures that transcend von Neumann limitations. We develop novel techniques including Quantum Synaptic Networks (QSN), Bio-Inspired Quantum Circuits (BIQC), Hybrid Plasticity Algorithms (HPA), and Neuromorphic-Quantum Interface Protocols (NQIP) that collectively enable AI systems with unprecedented cognitive capabilities. Through comprehensive theoretical analysis and simulation studies, we demonstrate that NQHL achieves 1000x energy efficiency improvements while maintaining 95% accuracy across diverse cognitive tasks including pattern recognition, causal reasoning, and creative problem-solving. Our framework successfully bridges the gap between biological intelligence principles and quantum computational advantages, providing the foundational architecture for post-digital AI systems that can operate at the scale and efficiency required for artificial general intelligence.
Artificial intelligence systems are shifting away to passive prediction systems into architectures that are supposed to rationalize and modify according to their needs as well as explain what they are doing. The current methods tend to maximize the precision at the expense of transparency and long-term flexibility, creating a gap in the systems with both accountable reasoning and learning processes. To solve this, we present an Agentic Hybrid Quantum -Neuromorphic AI system with an Explainable Reinforcement Learning core. It consists of a combination of quantum-assisted exploration, spiking neuromorphic adaptation, interpretable policy optimization to enable the system to adapt computational strategy depending on the dynamics of rewards, confidence, and environmental change. Experiments exhibit enhanced convergence speed, ability to maintain behavior in the case of sudden changes in the environment and a far greater enhancement in explanation coherence than traditional reinforcement learning agents and adaptive immersive systems.
Modern healthcare systems increasingly rely on artificial intelligence, deep learning, and medical imaging technologies for accurate disease diagnosis and clinical decision-making. However, conventional AI systems still face challenges related to interpretability, computational complexity, centralized data dependency, and patient privacy. This paper presents an Explainable Quantum Deep Learning Framework integrating Vision Transformers, Federated Intelligence, and Explainable Artificial Intelligence techniques for smart healthcare applications. The proposed framework combines quantum-inspired optimization, multimodal medical data analysis, transformer-based feature extraction, and decentralized federated learning to improve diagnostic accuracy, scalability, privacy preservation, and interpretability. Explainability mechanisms including SHAP and Grad-CAM are incorporated to enhance physician trust and clinical transparency. Experimental evaluation demonstrates superior performance in biomedical image classification and disease prediction compared with conventional deep learning models, providing a scalable solution for next-generation intelligent healthcare systems.