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.
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 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.
The relationship between quantum computing and the Internet of Things (IoT) is becoming a powerful pathway to develop secure, intelligent and sustainable network systems – however, present-day IoT architectures experience scalability challenges, as well as energy and data vulnerability, due to classical models in cryptography. This paper presents a Next Generation Quantum-IoT Communication Framework (QIoT-CF) that addresses these issues with quantum-based encryption, entanglement-assisted routing, and dynamic sustainability optimization for communication in the framework. The architecture uses Quantum Key Distribution (QKD) to ensure quality data security that is unbreakable, and it uses reinforcement learning–based queuing to balance energy consumption and communication reliability with heterogeneous IoT nodes. The simulations suggest that latency decreased by 45%, energy consumption improved by 43%, and the quantum bit error rate (QBER) improved by 52% relative to current IoT systems. The sustainability controller will change transmission policies based on carbon-aware energy metrics providing an acceptable trade-off between quantum performance and sustainability at-scale. The framework represents a critically significant advancement toward eco-intelligent quantum communication security situated within the 6G vision and beyond.
Abstract: This research explores the emerging paradigm of Quantum Intelligence (QI), which unifies artificial intelligence and quantum computing to enable next-generation cognitive systems. By leveraging quantum principles such as superposition, entanglement, and quantum tunneling, QI frameworks transcend classical AI limitations in learning efficiency, contextual awareness, and problem-solving complexity. The study implements a hybrid architecture integrating variational quantum circuits with reinforcement learning agents, tested across multi-goal environments. Experimental results reveal faster convergence, greater entropy reduction, and enhanced generalization compared to classical deep Q-networks. These findings demonstrate the transformative potential of QI to replicate and surpass human-like cognition, offering scalable, efficient, and adaptable models for future autonomous reasoning systems. Keywords: Quantum intelligence, Artificial intelligence, Quantum computing, Hybrid AI systems, Reinforcement learning, Variational quantum circuits, Cognitive systems, Quantum-enhanced learning, Entropy optimization, Next-generation AI
We propose an adaptive quantum routing protocol designed for dynamic network topologies in which link fidelity and availability vary stochastically over time. By introducing a novel Link Stability Metric that integrates predictive fidelity estimation with historical reliability analysis, the protocol can make real-time routing decisions that balance communication quality, latency and quantum resource consumption. The routing problem is formulated as a Markov Decision Process and solved using a deep reinforcement learning (RL) framework, enabling intelligent entanglement path selection and adaptive switching in response to channel degradation. We derive a fundamental bound on achievable end-to-end fidelity in dynamic quantum networks and provide convergence guarantees for the learning-based policy. Extensive simulations on scale-free and random geometric networks demonstrate significant improvements in success probability, fidelity and latency compared with state-of-the-art benchmarks, validating the theoretical analysis and showing the potential of adaptive learning approaches for robust, scalable quantum internetworking.
The emergence of sixth-generation (6G) wireless networks introduces unprecedented requirements for ultra-secure, low-latency communication across heterogeneous space–air–ground integrated network (SAGIN). Existing drone communication frameworks including LoRaWAN, Long Term Evolution, and Ad Hoc mesh architectures exhibit critical vulnerabilities to eavesdropping, jamming, and quantum-computational attacks due to their reliance on classical cryptographic primitives. To address these challenges, this work presents the Quantum-Secured Adaptive Routing Algorithm (QSARA), a novel framework designed for 6G-enabled unmanned aerial vehicle (UAV) networks that integrates Quantum Key Distribution (QKD), Reconfigurable Intelligent Surfaces (RIS), and Joint Communication and Sensing (JCAS) to enhance information-theoretic security and real-time performance. The proposed framework employs a quantum-augmented dynamic graph model to represent UAV swarm networking and uses Proximal Policy Optimisation (PPO)-based deep reinforcement learning to optimise routing under adversarial and uncertain conditions. A multi-objective cost function jointly captures classical quality of service metrics, such as latency, bandwidth, and energy consumption alongside with quantum-layer security indicators, including quantum bit error rate, key pool entropy, and key availability. High-fidelity simulations with 500 mobile drones under diverse adversarial threats demonstrate that the proposed framework achieves a key establishment success rate of 96.2%, end-to-end latency of 23.7 milliseconds, energy consumption of 7.8 watt-hours, and a packet delivery ratio of 94.1%, outperforming state-of-the-art classical and quantum-aware baselines. These results position the QSARA as a scalable and quantum-resilient routing solution for mission-critical UAV networking in next-generation 6G smart mobility ecosystems.
This paper proposes a dynamic routing protocol for quantum networks based on the fluctuating entanglement states between quantum nodes. The core of the protocol lies in real-time monitoring of entanglement strength and dynamically adjusting routing paths based on these changes. This approach aims to mitigate the risk of information loss due to entanglement decay, ultimately achieving high reliability in quantum routing. Unlike traditional protocols relying on fixed topologies, this protocol adapts to the dynamic nature of entanglement, enhancing routing efficiency and resilience. The protocol utilizes a graph-based representation of the quantum network and employs algorithms for entanglement assessment and path optimization. Mathematical models are presented to formalize the routing process, including the calculation of entanglement fidelity and path cost. The proposed system demonstrates improved routing performance and fault tolerance compared to static routing schemes. The key contribution is a dynamic, adaptive routing strategy that leverages the inherent properties of quantum networks for reliable quantum information transmission.