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Quantum-Augmented Hybrid Routing in Dynamic Networks: A Physics-Inspired Reinforcement Learning Approach

Haithem Abdelghany, Mohamed M. Ashour

Published Aug 6, 2025• 0 citations
Publisher

Abstract

Abstract The innovative dynamics of communication networks are being challenged unprecedentedly by rising data traffic, dynamically evolving topologies, and the increased complexity of distributed intelligent applications. Typically, routing algorithms are inadequate for adjusting in actual time to address network congestion, changes in computational needs and heterogeneity and real-time. In this paper, we propose a new hybrid quantum-augmented routing model that synergistically integrates quantum reinforcement learning with physics-motivated network modeling by reasoning in terms of a space‒time continuum. Here, the phenomenon of network congestion is simulated as a curvature on the fabric of space-time, and the computation facilities on nodes are envisioned as a gravitational source, which directs the flows of data. The most essential innovation is the development of an artificial intelligence awareness layer (AIAL), that functions with quantum-aid learning agents proactively weighing and choosing the routing paths in live environments. The quantum agents query the multiple parallel paths in SQL via quantum superposition and quantum parallelism to enhance convergence and global route optimization. The hybrid structure is built upon features of the local adaptability of quantum-enhanced Q-learning and global contextual knowledge of deep Q-networks (DQNs), which are constantly adjusted to changes in network metrics, such as the load, latency, and availability of nodes. Large simulations demonstrate that this structure has a reward/cost ratio of 138.16, scales well during states of extreme congestion and is structurally efficient in all cases, with higher resiliency and faster convergence of the framework than those of OSPF, the EIGRP, the IS-IS and the RIP. Although this is a positive finding, it is reached under certain simulation conditions and it is not directly applicable in real-life network with diverse topologies and traffic patterns. The findings corroborate that coupling quantum reinforcement learning with physics-inspired routing will make full use of the adaptive optimal routing discoverable in uncertain situations, changing network conditions in an autonomous manner. This study establishes a basis for a new generation of cognitively adaptive, self-optimizing networks to best meet the needs of 6G, edge computing and quantum-aware distributed infrastructures.

DOI
10.21203/rs.3.rs-7124150/v1

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Abstract The innovative dynamics of communication networks are being challenged unprecedentedly by rising data traffic, dynamically evolving topologies, and the increased complexity of distributed intelligent applications. Typically, routing algorithms are inadequate for adjusting in actual time to address network congestion, changes in computational needs and heterogeneity and real-time. In this paper, we propose a new hybrid quantum-augmented routing model that synergistically integrates quantum reinforcement learning with physics-motivated network modeling by reasoning in terms of a space‒time continuum. Here, the phenomenon of network congestion is simulated as a curvature on the fabric of space-time, and the computation facilities on nodes are envisioned as a gravitational source, which directs the flows of data. The most essential innovation is the development of an artificial intelligence awareness layer (AIAL), that functions with quantum-aid learning agents proactively weighing and choosing the routing paths in live environments. The quantum agents query the multiple parallel paths in SQL via quantum superposition and quantum parallelism to enhance convergence and global route optimization. The hybrid structure is built upon features of the local adaptability of quantum-enhanced Q-learning and global contextual knowledge of deep Q-networks (DQNs), which are constantly adjusted to changes in network metrics, such as the load, latency, and availability of nodes. Large simulations demonstrate that this structure has a reward/cost ratio of 138.16, scales well during states of extreme congestion and is structurally efficient in all cases, with higher resiliency and faster convergence of the framework than those of OSPF, the EIGRP, the IS-IS and the RIP. Although this is a positive finding, it is reached under certain simulation conditions and it is not directly applicable in real-life network with diverse topologies and traffic patterns. The findings corroborate that coupling quantum reinforcement learning with physics-inspired routing will make full use of the adaptive optimal routing discoverable in uncertain situations, changing network conditions in an autonomous manner. This study establishes a basis for a new generation of cognitively adaptive, self-optimizing networks to best meet the needs of 6G, edge computing and quantum-aware distributed infrastructures.
This paper introduces a quantum-hybrid-aware routing framework that incorporates the concepts of quantum computing with meta-cognitive learning to improve adaptability, reliability, and awareness in dynamic quantum networks. The proposed system accommodates a new IoT, edge computing and 6G system, where routing schemes should be flexible and robust and have cross-topology functionality. Conventional methods of quantum reinforcement learning can be characterized by a trade-off between exploration and decision stability. To cope with this, the proposed self-aware hybrid architecture is a combination of quantum-enhanced learning and a meta-cognitive awareness layer to allow autonomous agents to monitor performance and adjust routing strategies on the fly. Experimental testing of the mesh, cluster, ring and star quantum topologies revealed stable and topologically silent behavior, and selfconfidence (96.1%) and awareness of errors (91.7) were constant across all environments. Statistical simulations (using 2,000 samples per topology) confirmed that whereas protocols specialized in quantum protocols such as the q_key_distribution (maximum quantum score of 0.6634 ± 0.0940) and the q_entanglement and q_teleportation (highest success in pair generation and teleportation) protocols were the most successful, the q_hybrid-aware protocol was the most balanced with respect to multimetric performance (and lowest fidelity variation of 0.005) and adaptive resilience. Scalability The scalability tests for 200 nodes revealed only a small 5.6x scaling factor, which confirmed the efficiency at a large scale or with embedded quantum controllers. The results confirm that quantum-aware routing performance must have context-based protocol flexibility and not be optimized.
Abstract Entanglement-based quantum networks require intelligent routing mechanisms that not only maximize fidelity and connectivity but also ensure service differentiation under varying traffic conditions. In this work, we propose a novel QoS-aware traffic modeling and scheduling framework integrated into a reinforcement learning-based quantum routing agent. Unlike prior approaches that assume deterministic or uniform request arrival patterns, our model introduces stochastic and bursty traffic scenarios using Erlang and ON/OFF distributions to emulate realistic quantum workloads. Each entanglement request is assigned a set of QoS attributes, including fidelity requirements, time-to-live constraints, and priority levels. We then extend the reward function of the agent to consider deadline compliance and resource efficiency in addition to fidelity. Through simulation, we compare the proposed method against state-of-the-art baselines, including DQRA and Proactive RL, under both best-effort and QoS-prioritized scheduling modes. Results demonstrate that our approach maintains higher success rates and lower latency for high-priority requests, while preserving entanglement fidelity and efficient resource allocation across varying network loads. This study highlights the importance of integrating QoS mechanisms into quantum routing to support scalable and differentiated quantum services
The integration of Quantum-Inspired Reinforcement Learning (QiRL) into Edge-Driven CyberPhysical Systems (CPS) presents a transformative approach to enhance real-time decision-making in distributed environments. While classical reinforcement learning (RL) struggles with scalability and computational complexity in edge-CPS, quantum-inspired Quantum-Inspired Reinforcement Learning for Edge-Driven Cyber-Physical Systems algorithms leverage superposition and entanglement principles to optimize policy exploration and convergence. This paper proposes a novel QiRL framework that combines quantum-inspired optimization with deep RL, enabling efficient resource allocation and adaptive control in latency-sensitive applications (e.g., smart grids, autonomous drones). We formalize the mathematical foundations of QiRL, design a hybrid neural-quantum architecture for edge deployment, and evaluate its performance under dynamic CPS constraints. Simulations on IoT-based case studies demonstrate 20−35%\mathbf{2 0}-\mathbf{3 5 \%}20−35% faster convergence and 15%\mathbf{1 5 \%}15% higher energy efficiency compared to classical Deep Q-Networks (DQN). Key challenges, such as quantum noise simulation and edge-hardware limitations, are discussed to guide future research. This work bridges quantum computing principles with edge intelligence, offering a scalable pathway for nextgeneration CPS.
In large-scale heterogeneous communication networks, routing optimization involves complex demands such as high-dimensional state spaces, multiple constraints, and real-time dynamics. A quantum-classical hybrid computing paradigm, leveraging quantum superposition and entanglement, offers a promising pathway to efficiently address NP-hard routing problems. By analyzing representative routing scenarios, a modeling framework for quantum algorithms was outlined, and key technical obstacles in applying quantum approaches to network routing were systematically summarized. These include conflicts between structural constraints and model expressiveness, biases in multi-objective formulations and delayed responses, the trade-off between diversity and convergence in high-dimensional solution spaces, and limited generalization capability under dynamic conditions. To overcome these issues, several directions were proposed: algorithmic expressiveness was enhanced through improved structural modeling, evolutionary design, and integrated scheduling strategies; computational accuracy and responsiveness were improved via multi-objective optimization, modular decoupling, and tight quantum-classical interaction; convergence and diversity were balanced through hybrid architectural design, evolutionary policy adaptation, and structural constraint management; and adaptability and generalization in dynamic environments were boosted by incorporating meta-learning, task decomposition, and structural transfer techniques. Furthermore, the co-development of quantum algorithms and hardware architectures were advocated, paving the way for practical deployment of quantum computing in communication networks within the NISQ era.
Quantum routing is crucial for future networks as it utilizes entanglement and swapping to establish efficient communication paths between nodes. Although machine learning has transformed various industries, its integration with quantum networking remains relatively underexplored. To address this gap, we present the Deep Quantum Routing Agent (DQRA), an innovative deep reinforcement learning framework that optimizes routing paths to maximize request fulfillment within constrained time windows. Our approach integrates a neural network with a qubit-preserving shortest-path algorithm, employing a reward-based training system to enhance connection success rates. Extensive experiments show DQRA achieves over 80% routing success in resource-limited networks and maintains approximately 60% effectiveness under challenging single-repeater conditions. Importantly, the model demonstrates polynomial-time complexity, ensuring scalable deployment and effectively merging machine learning with quantum network optimization
Vehicle Routing Problem (VRP) is one of the most complex NP-hard combinatorial optimization problem in transportation and logistics that requires a dynamic solution approach. In this paper we present a new hybrid approach that combines the Quantum Approximate Optimization Algorithm (QAOA) into the QRL policy network, instead of the usual variational layers, QAOA mixing and cost Hamiltonian layers. This enhancement enables the agent to exploit problem specific particular quantum correlations when learning policies, and so richer exploration of the routing solution space. The QAOA-augmented QRL framework shows quicker convergence in training and can tackle larger VRP instances that are beyond the reach of Grover's Adaptive Search (GAS) and Quantum Reinforcement Learning (QRL) approaches. Experiments on standard VRP instances demonstrate better solutions, fewer episodes to converge and good memory usage on near term quantum hardware simulators. These findings demonstrate QAOA- integrated QRL as a viable approach to scalable, high quality quantum-assisted combinatorial optimization.