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

Advances in computational intelligence and robotics book series

Quantum-Inspired or Neuromorphic Discrete-Event Computing Paradigms for Future Ubiquitous Systems

Shrishail Math, H. D. Madhuri, Vijaykumar Yadhav, Mallanagouda Patil, P. Selvakumar

Published Jun 5, 2026• 1 citation
Publisher

Abstract

Foundations of quantum-inspired discrete-event architectures constitute an emerging and transformative paradigm in the modelling, simulation, and design of complex, dynamic, and stochastic systems, particularly in the context of large-scale distributed networks, Internet of Things (IoT) ecosystems, cyber-physical systems, cloud and edge computing, service-oriented architectures, and high-performance computing environments, where traditional discrete-event simulation (DES) approaches face challenges related to scalability, uncertainty, combinatorial complexity, and real-time decision-making; quantum-inspired methodologies draw conceptual and algorithmic insights from quantum mechanics, quantum computing principles, and quantum information theory, including superposition, entanglement, probabilistic amplitudes, and parallelism, to enhance the capability of DES frameworks to represent, process, and optimize highly complex event-driven interactions across heterogeneous devices, services, and computing layers.

DOI
10.4018/979-8-3373-9785-6.ch004

Related papers

Quantum Optimization of Event-Driven Neuromorphic Systems

Shweta Dour

Neuromorphic computing is inspired by the dynamics of biological neural systems - they operate event-driven and enable energy-efficient and real-time learning by Spiking neural networks. Advantages of this approach include faster convergence, improved accuracy, and potential energy efficiency gain, making this approach suitable for edge AI, real-time signal processing, and large-scale cognitive simulations. The convergence of neuromorphic computing and quantum optimization offers a transformative pathway for next-generation AI. By integrating event-driven spiking neural networks with quantum algorithms, hybrid systems can achieve faster learning, enhanced global optimization, and energy-efficient computation beyond classical limits. A comprehensive framework has been presented for designing and analyzing such hybrid quantum-neuromorphic architectures, highlighting their potential for real-time, scalable, and intelligent applications.

Advances in computational intelligence and robotics book series•Mar 4, 2026•0 citations

Integrating Neuromorphic and Quantum Computing Paradigms for Real-Time Internet of Things Analytics

Harish Reddy Gantla, Jitender Jain, K. Arunasakthi +3 more

The explosive increase in the number of Internet of Things (IoT) deployments has increased the pressure on analytics systems that are capable of functioning in real time without being power-intensive and scalable in the presence of unlimited amounts of data. Traditional cloud-based and even edge-based AI systems are unable to strike a balance between the low-latency requirements and the in-depth analytical reasoning, especially when the level of data uncertainty and the scale of the system grows. The hybrid neuromorphic-quantum analytics model suggested in this paper integrates event-driven spiking neuromorphic quantum-assisted inference selectively invoked on complex or uncertain cases, that is, on the edge with event-driven spiking neural networks. The neuromorphic processing deals with inference with high frequency and low latency, and quantum analytics is triggered because of the confidencebased orchestration to decide on ambivalent pattern and global correlations.

Advances in computational intelligence and robotics book series•Mar 4, 2026•0 citations

Quantum Computing and IOT Integration For Next-Generation Smart Systems

T. Vamshi Mohana, Panji Anugrah Sampurna, Salma Begum

The rapid expansion of the Internet of Things (IoT) has transformed the way physical devices communicate, process information, and provide intelligent services.With billions of interconnected devices generating enormous volumes of heterogeneous data, conventional computing architectures increasingly face limitations in computational efficiency, security, optimisation, and scalability.Quantum computing has emerged as a disruptive paradigm capable of solving computationally intensive problems exponentially faster than classical computers for selected problem classes.Integrating quantum computing with IoT offers unprecedented opportunities for intelligent decision-making, real-time optimisation, secure communication, and enhanced machine learning.Quantum-enhanced IoT (QIoT) combines quantum algorithms, quantum machine learning, quantum communication, and post-quantum cryptography to create highly efficient and secure smart systems.Applications span healthcare, industrial automation, smart cities,

IARJSET•Jul 23, 2026•0 citations

Quantum-Inspired Mathematical Models for Complex Decision Systems

S. Sathiyanbabu, P. Dhanalakshmi, Dr. R. Venugopal +1 more

The increasing complexity of modern decision-making environments has challenged the effectiveness of classical mathematical models. Traditional probabilistic frameworks often struggle to represent uncertainty, ambiguity, contextual dependence, and nonlinear interactions among decision variables. Quantum-inspired mathematical models have emerged as a promising interdisciplinary approach that leverages mathematical principles derived from quantum theory without requiring quantum computing hardware. These models utilize concepts such as superposition, interference, entanglement, and contextuality to represent complex decision processes more accurately. This paper explores the theoretical foundations, mathematical structures, and applications of quantum-inspired decision models in complex systems. The study examines how quantum probability theory provides a more flexible representation of human cognition, organizational decision-making, financial forecasting, and artificial intelligence systems. Furthermore, the paper discusses mathematical formulations, implementation strategies, advantages, challenges, and future research directions. The findings suggest that quantum-inspired models offer significant potential for addressing uncertainty and dynamic interactions in complex decision environments, thereby contributing to the advancement of intelligent decision support systems.

Stanzaleaf International Journal of Multidisciplinary Studies•Jun 12, 2026•0 citations

Quantum-Inspired Meta-Heuristic Algorithm for Large-Scale Graph Neural Network Training in Distributed Cloud-Edge Environments

Eka Prasetya Adhu Sugara, Nurul Azwanti, Ivy Derla

This paper explores the application of quantum-inspired optimization algorithms in the training of large-scale Graph Neural Networks (GNNs) within distributed cloud-edge environments. GNNs have gained significant attention due to their ability to model complex relationships in graph-structured data, yet their training presents challenges such as high computational demand, inefficient resource allocation, and slow convergence, especially for large datasets. Traditional meta-heuristic algorithms, while useful, often face scalability and performance issues when applied to such large-scale tasks. To address these challenges, we propose a quantum-inspired meta-heuristic algorithm that leverages quantum principles, such as superposition and entanglement, to enhance optimization processes. The algorithm was integrated into a hybrid cloud-edge system, where computational tasks are dynamically distributed between edge nodes and the cloud, optimizing resource utilization and reducing latency. Our experimental results demonstrate significant improvements in training speed, resource efficiency, and convergence rate when compared to traditional optimization methods such as Genetic Algorithms and Simulated Annealing. The quantum-inspired algorithm not only accelerates the training process but also reduces memory usage, making it well-suited for large-scale GNN applications. Furthermore, the system's scalability was enhanced by the hybrid cloud-edge architecture, which balances computational load and enables real-time data processing. The findings suggest that quantum-inspired optimization algorithms can significantly improve the training of GNNs in distributed systems, opening new avenues for real-time applications in areas such as social network analysis, anomaly detection, and recommendation systems. Future work will focus on refining these algorithms to handle even larger datasets and more complex GNN architectures, with potential integration into edge devices for enhanced real-time decision-making.

Global Science Journal of Information Technology and Computer Science•Jun 30, 2025•1 citation

Quantum Simulators: Architectures and Opportunities

Ehud Altman, Kenneth R. Brown, Giuseppe Carleo +34 more

Quantum simulators are a promising technology on the spectrum of quantum devices from specialized quantum experiments to universal quantum computers. These quantum devices utilize entanglement and many-particle behaviors to explore and solve hard scientific, engineering, and computational problems. Rapid development over the last two decades has produced more than 300 quantum simulators in operation worldwide using a wide variety of experimental platforms. Recent advances in several physical architectures promise a golden age of quantum simulators ranging from highly optimized special purpose simulators to flexible programmable devices. These developments have enabled a convergence of ideas drawn from fundamental physics, computer science, and device engineering. They have strong potential to address problems of societal importance, ranging from understanding vital chemical processes, to enabling the design of new materials with enhanced performance, to solving complex computational problems. It is the position of the community, as represented by participants of the NSF workshop on "Programmable Quantum Simulators," that investment in a national quantum simulator program is a high priority in order to accelerate the progress in this field and to result in the first practical applications of quantum machines. Such a program should address two areas of emphasis: (1) support for creating quantum simulator prototypes usable by the broader scientific community, complementary to the present universal quantum computer effort in industry; and (2) support for fundamental research carried out by a blend of multi-investigator, multi-disciplinary collaborations with resources for quantum simulator software, hardware, and education.

arXiv:1912.06938•PRX Quantum•Feb 24, 2021•581 citations
Foundations of quantum-inspired discrete-event architectures constitute an emerging and transformative paradigm in the modelling, simulation, and design of complex, dynamic, and stochastic systems, particularly in the context of large-scale distributed networks, Internet of Things (IoT) ecosystems, cyber-physical systems, cloud and edge computing, service-oriented architectures, and high-performance computing environments, where traditional discrete-event simulation (DES) approaches face challenges related to scalability, uncertainty, combinatorial complexity, and real-time decision-making; quantum-inspired methodologies draw conceptual and algorithmic insights from quantum mechanics, quantum computing principles, and quantum information theory, including superposition, entanglement, probabilistic amplitudes, and parallelism, to enhance the capability of DES frameworks to represent, process, and optimize highly complex event-driven interactions across heterogeneous devices, services, and computing layers.
Neuromorphic computing is inspired by the dynamics of biological neural systems - they operate event-driven and enable energy-efficient and real-time learning by Spiking neural networks. Advantages of this approach include faster convergence, improved accuracy, and potential energy efficiency gain, making this approach suitable for edge AI, real-time signal processing, and large-scale cognitive simulations. The convergence of neuromorphic computing and quantum optimization offers a transformative pathway for next-generation AI. By integrating event-driven spiking neural networks with quantum algorithms, hybrid systems can achieve faster learning, enhanced global optimization, and energy-efficient computation beyond classical limits. A comprehensive framework has been presented for designing and analyzing such hybrid quantum-neuromorphic architectures, highlighting their potential for real-time, scalable, and intelligent applications.
The explosive increase in the number of Internet of Things (IoT) deployments has increased the pressure on analytics systems that are capable of functioning in real time without being power-intensive and scalable in the presence of unlimited amounts of data. Traditional cloud-based and even edge-based AI systems are unable to strike a balance between the low-latency requirements and the in-depth analytical reasoning, especially when the level of data uncertainty and the scale of the system grows. The hybrid neuromorphic-quantum analytics model suggested in this paper integrates event-driven spiking neuromorphic quantum-assisted inference selectively invoked on complex or uncertain cases, that is, on the edge with event-driven spiking neural networks. The neuromorphic processing deals with inference with high frequency and low latency, and quantum analytics is triggered because of the confidencebased orchestration to decide on ambivalent pattern and global correlations.
The rapid expansion of the Internet of Things (IoT) has transformed the way physical devices communicate, process information, and provide intelligent services.With billions of interconnected devices generating enormous volumes of heterogeneous data, conventional computing architectures increasingly face limitations in computational efficiency, security, optimisation, and scalability.Quantum computing has emerged as a disruptive paradigm capable of solving computationally intensive problems exponentially faster than classical computers for selected problem classes.Integrating quantum computing with IoT offers unprecedented opportunities for intelligent decision-making, real-time optimisation, secure communication, and enhanced machine learning.Quantum-enhanced IoT (QIoT) combines quantum algorithms, quantum machine learning, quantum communication, and post-quantum cryptography to create highly efficient and secure smart systems.Applications span healthcare, industrial automation, smart cities,
The increasing complexity of modern decision-making environments has challenged the effectiveness of classical mathematical models. Traditional probabilistic frameworks often struggle to represent uncertainty, ambiguity, contextual dependence, and nonlinear interactions among decision variables. Quantum-inspired mathematical models have emerged as a promising interdisciplinary approach that leverages mathematical principles derived from quantum theory without requiring quantum computing hardware. These models utilize concepts such as superposition, interference, entanglement, and contextuality to represent complex decision processes more accurately. This paper explores the theoretical foundations, mathematical structures, and applications of quantum-inspired decision models in complex systems. The study examines how quantum probability theory provides a more flexible representation of human cognition, organizational decision-making, financial forecasting, and artificial intelligence systems. Furthermore, the paper discusses mathematical formulations, implementation strategies, advantages, challenges, and future research directions. The findings suggest that quantum-inspired models offer significant potential for addressing uncertainty and dynamic interactions in complex decision environments, thereby contributing to the advancement of intelligent decision support systems.
This paper explores the application of quantum-inspired optimization algorithms in the training of large-scale Graph Neural Networks (GNNs) within distributed cloud-edge environments. GNNs have gained significant attention due to their ability to model complex relationships in graph-structured data, yet their training presents challenges such as high computational demand, inefficient resource allocation, and slow convergence, especially for large datasets. Traditional meta-heuristic algorithms, while useful, often face scalability and performance issues when applied to such large-scale tasks. To address these challenges, we propose a quantum-inspired meta-heuristic algorithm that leverages quantum principles, such as superposition and entanglement, to enhance optimization processes. The algorithm was integrated into a hybrid cloud-edge system, where computational tasks are dynamically distributed between edge nodes and the cloud, optimizing resource utilization and reducing latency. Our experimental results demonstrate significant improvements in training speed, resource efficiency, and convergence rate when compared to traditional optimization methods such as Genetic Algorithms and Simulated Annealing. The quantum-inspired algorithm not only accelerates the training process but also reduces memory usage, making it well-suited for large-scale GNN applications. Furthermore, the system's scalability was enhanced by the hybrid cloud-edge architecture, which balances computational load and enables real-time data processing. The findings suggest that quantum-inspired optimization algorithms can significantly improve the training of GNNs in distributed systems, opening new avenues for real-time applications in areas such as social network analysis, anomaly detection, and recommendation systems. Future work will focus on refining these algorithms to handle even larger datasets and more complex GNN architectures, with potential integration into edge devices for enhanced real-time decision-making.
Quantum simulators are a promising technology on the spectrum of quantum devices from specialized quantum experiments to universal quantum computers. These quantum devices utilize entanglement and many-particle behaviors to explore and solve hard scientific, engineering, and computational problems. Rapid development over the last two decades has produced more than 300 quantum simulators in operation worldwide using a wide variety of experimental platforms. Recent advances in several physical architectures promise a golden age of quantum simulators ranging from highly optimized special purpose simulators to flexible programmable devices. These developments have enabled a convergence of ideas drawn from fundamental physics, computer science, and device engineering. They have strong potential to address problems of societal importance, ranging from understanding vital chemical processes, to enabling the design of new materials with enhanced performance, to solving complex computational problems. It is the position of the community, as represented by participants of the NSF workshop on "Programmable Quantum Simulators," that investment in a national quantum simulator program is a high priority in order to accelerate the progress in this field and to result in the first practical applications of quantum machines. Such a program should address two areas of emphasis: (1) support for creating quantum simulator prototypes usable by the broader scientific community, complementary to the present universal quantum computer effort in industry; and (2) support for fundamental research carried out by a blend of multi-investigator, multi-disciplinary collaborations with resources for quantum simulator software, hardware, and education.