Abstract Quantum networks provide both security and faster communication; however, they face critical challenges in terms of dynamic resource allocation and maintaining robust security. Current static allocation methodologies frequently lead to excessive latency and attack vulnerabilities, which restrict performance and scalability. To address these challenges, this article presents a secure and adaptive resource allocation outline for quantum networks, merging quantum-enhanced security protocols with real-time optimization. The framework utilizes Quantum Key Distribution - Single Center Method (QKD-SCM) to exchange secure and scalable keys. and the Stochastic Programming and the Cooperative-Qubit-Allocation-Problem (SP-CQAP), which addresses dynamic resource allocations. High-fidelity entangled resources are managed with the Fidelity Maintenance Scheme Based on Prioritized Purification (FMSPP), and congestion is avoided using Self-Adaptive and Content-Based Scheduling (CACS). The proposed framework was evaluated using NS-3.30.1 and Python 3.7.4, running on Ubuntu 18.4 LTS. In comparison to already-existing methodologies within the segment, the proposed framework demonstrated reduced latency of 20 ms, energy efficiencies of 90%, a quantum key distribution success rate of 90%, and resource utilization equating to 70%. The findings demonstrate the promise of quantum-enhanced wireless communication through dynamic resource allocation for safe, low-latency, and high-performance networking. Removed: security enhancement implied earlier.
Abstract Quantum networks provide both security and faster communication; however, they face critical challenges in terms of dynamic resource allocation and maintaining robust security. Current static allocation methodologies frequently lead to excessive latency and attack vulnerabilities, which restrict performance and scalability. To address these challenges, this article presents a secure and adaptive resource allocation outline for quantum networks, merging quantum-enhanced security protocols with real-time optimization. The framework utilizes Quantum Key Distribution - Single Center Method (QKD-SCM) to exchange secure and scalable keys. and the Stochastic Programming and the Cooperative-Qubit-Allocation-Problem (SP-CQAP), which addresses dynamic resource allocations. High-fidelity entangled resources are managed with the Fidelity Maintenance Scheme Based on Prioritized Purification (FMSPP), and congestion is avoided using Self-Adaptive and Content-Based Scheduling (CACS). The proposed framework was evaluated using NS-3.30.1 and Python 3.7.4, running on Ubuntu 18.4 LTS. In comparison to already-existing methodologies within the segment, the proposed framework demonstrated reduced latency of 20 ms, energy efficiencies of 90%, a quantum key distribution success rate of 90%, and resource utilization equating to 70%. The findings demonstrate the promise of quantum-enhanced wireless communication through dynamic resource allocation for safe, low-latency, and high-performance networking. Removed: security enhancement implied earlier.
The fast growth of Internet of Things (IoT) networks has heightened the demand for secure, scalable and energy efficient communications methods. Traditional cryptographic techniques are more susceptible to quantum computing attacks and therefore have pushed the necessity for quantum secure methods. This study presents an energy efficient artificial intelligence assisted quantum secure communication framework designed to scale and provide reliable IoT networks. The framework uses Quantum Key Distribution (QKD) combined with deep learning optimization to improve both relationship and network performance. A self-adaptive AI module applies its lower level of computational resources and dynamically changing network conditions in order to optimize key management, routing decisions, and resource allocation, which reduces both energy consumption and communication latency. The proposed system is intended to support the specific types of constraints faced within an IoT environment. The experimental simulation results demonstrate that the framework improves energy efficiency, increases packet delivery ratio, and decreases latency in IVN (Intelligent Vehicular Networks) by comparison with traditional means of secure communication. In addition, by combining AI and quantum communications, we increase our resilience to threats posed by both classical and quantum forms of attack while providing an added layer of protection for transmitting data between devices in future wireless networks. Additionally, this proposed framework will provide a very scalable and effective method for providing IoT solutions within the environment of 5G mobile networks or later generations.
This paper presents a novel algorithm for optimizing the allocation of entangled resources within quantum networks. The core of the approach lies in employing a reinforcement learning (RL) framework to dynamically manage and distribute entanglement, addressing the inherent challenges of maximizing utilization and enhancing quantum communication efficiency. Unlike traditional static allocation methods, the proposed RL agent learns optimal allocation strategies through iterative interaction with a simulated quantum network environment. The algorithm considers factors such as node connectivity, qubit availability, and communication delays to minimize resource wastage and improve overall network performance. The key performance indicator (KPI) is the entanglement fidelity between communicating nodes. The developed algorithm offers a promising pathway towards realizing the full potential of quantum networks by intelligently managing their most valuable asset: entanglement. The primary goal is to achieve higher fidelity entanglement distribution and improved communication rates. The effectiveness of the algorithm is evaluated through simulations, demonstrating its ability to outperform existing allocation schemes in various network topologies and operational scenarios.
Wireless communication systems face increasing challenges in resource allocation, latency reduction, scalability, and adaptation because modern networks generate high-dimensional, nonlinear, and rapidly changing data. This work presents a hybrid Quantum Neural Network (QNN)-driven framework for intelligent resource optimization in dynamic wireless communication environments. The proposed framework integrates variational quantum circuits with classical optimization techniques to support adaptive learning and efficient decision-making. A modular architecture consisting of User, Database, and Operator modules enables secure data acquisition, storage, preprocessing, continuous model training, and operator-driven optimization. Quantum Neural Networks are employed as the primary learning mechanism, while hybrid quantum-classical optimization supports stable parameter training. Quantum annealing is incorporated for complex tasks such as channel allocation, bandwidth management, and resource scheduling. The framework is designed to improve allocation accuracy, reduce latency, enhance scalability, reliability, security, and resource utilization. Continuous learning further enables adaptation to changing traffic patterns, user requirements, workloads, and network conditions. Keywords: Quantum Neural Network, Wireless Communication, Resource Optimization, Quantum Computing, Hybrid Quantum-Classical Learning, Resource Allocation.
Quantum communication networks have emerged as a promising solution for secure information exchange using quantum mechanical principles such as entanglement and quantum key distribution (QKD). However, most existing multi-user quantum communication frameworks rely on static protocol execution strategies that continuously perform reconciliation and verification procedures irrespective of real-time communication conditions. Such behaviour increases communication overhead, latency accumulation, and resource consumption under dynamically varying network environments. This paper proposes an adaptive QBERaware protocol orchestration framework for secure and scalable multi-user quantum communication networks. This work continuously monitors Quantum Bit Error Rate (QBER), channel noise, communication delay, and traffic density to dynamically regulate communication execution intensity according to network stability conditions. A lightweight threshold-driven orchestration mechanism is introduced to selectively activate communication and security procedures while preserving transmission integrity. Experimental evaluation demonstrates that this work reduces protocol overhead, improves scalability, minimizes communication latency, and enhances adaptive responsiveness when compared with conventional static execution approaches. This work architecture provides a computationally efficient and adaptive communication management framework suitable for next-generation distributed quantum networking infrastructures.
The demand for secure communication in the age of quantum technologies has driven progress in quantum key distribution (QKD) techniques for optical networks. This research addresses the issues of high blocking probabilities (BPs) and the proper utilization of quantum resources in varying network loads by introducing a novel heuristic approach, termed dynamic security-aware quantum resource allocation (D-SQRA), designed for dynamic resource allocation in QKD-enabled optical networks. We propose two D-SQRA algorithms to employ an adaptive resource assignment (RA) strategy that concurrently addresses routing, wavelength, and time-slot selection while dynamically modifying security levels according to the real-time network load and resource availability. We evaluate the proposed D-SQRA performance against two conventional methods, namely, fixed security quantum resource allocation (F-SQRA) and baseline quantum resource allocation (B-QRA). We discuss the results for NSFNET and UBN24 topologies for network security performance metrics such as network security performance (NSP), BP, quantum key utilization (QKU), and time-slot utilization. The results show that the proposed D-SQRA algorithms provide significant improvement with respect to conventional techniques in addressing proper resource utilization and management by reducing BPs of the new incoming connection requests.
This paper addresses the critical challenge of optimizing resource allocation within dynamic quantum networks (QNs). The inherent complexities of QNs, including fluctuating demands, evolving topologies, and the fragility of quantum states, necessitate adaptive resource management strategies. We propose a novel algorithm leveraging reinforcement learning (RL) to dynamically adjust resource allocation based on real-time network conditions. The algorithm learns an optimal policy by iteratively interacting with a simulated QN environment, maximizing the overall quantum channel throughput while minimizing the probability of quantum error. Specifically, the core mechanism involves formulating the resource allocation problem as a Markov Decision Process (MDP), where the state represents the network's current configuration, the action represents the allocation of quantum resources (e.g., qubits, entanglement), and the reward reflects the successful completion of quantum communication tasks. The RL agent utilizes a Q-learning algorithm to estimate the optimal Q-function, guiding the allocation process. We demonstrate, through simulation, that our algorithm significantly outperforms static allocation strategies in terms of throughput and reliability, even under highly dynamic network conditions. The key contributions of this work lie in the integration of RL with the unique characteristics of QNs and the development of a practical algorithm for adaptive resource management. Further research will explore the scalability of this approach and its application to larger, more complex QN topologies.