Quantum communication ensures that data remains secure from unauthorized access. Despite these advantages, this technology faces several challenges. For instance, photons can be lost when they go through channels, and there is a chance of eavesdropping, that make the system not as trustworthy, safe, and efficient. To make quantum communication systems function effectively in the real world, we need to combine advanced technologies like quantum error correction mechanisms and quantum repeaters. This work suggests a unified Quantum Inspired Genetic Algorithm (QIGA) framework that utilizes collaborative quantum repeaters to conquer the limitations of network links over distance. This study introduces a hybrid optimization topology (OT) that integrates mesh, star, and ring configurations for measurement device-independent quantum key distribution (MDI-QKD) networks. This approach allows for the efficient use of resources for scalable quantum communication networks. The proposed network architecture integrates 12 user nodes, a trusted node, and a single shared quantum repeater within a hybrid topology. The repeater deployment has been designed to support links that are longer than 52 km. This design accommodates deployments on various scales, small, medium, and large, while optimizing resource usage and infrastructure costs. We evaluated the performance and security trade offs in quantum communication networks by optimizing three topologies with a QIGA: ring, ring-star, and ring-star-mesh hybrid (QIGA−OT). The optimization methodology aims to reconcile essential performance metrics, such as the Secure Key Rate (SKR), Quantum Bit Error Rate (QBER), channel loss, and Merit of Quantum Efficiency (MQE). MQE functions as a metric for evaluating trades concerning security, efficiency, and cost-effectiveness within the network. This work presents a novel approach for utilizing quantum-inspired in the effective design and implementation of scalable network topologies across numerous fields of application.
Quantum communication ensures that data remains secure from unauthorized access. Despite these advantages, this technology faces several challenges. For instance, photons can be lost when they go through channels, and there is a chance of eavesdropping, that make the system not as trustworthy, safe, and efficient. To make quantum communication systems function effectively in the real world, we need to combine advanced technologies like quantum error correction mechanisms and quantum repeaters. This work suggests a unified Quantum Inspired Genetic Algorithm (QIGA) framework that utilizes collaborative quantum repeaters to conquer the limitations of network links over distance. This study introduces a hybrid optimization topology (OT) that integrates mesh, star, and ring configurations for measurement device-independent quantum key distribution (MDI-QKD) networks. This approach allows for the efficient use of resources for scalable quantum communication networks. The proposed network architecture integrates 12 user nodes, a trusted node, and a single shared quantum repeater within a hybrid topology. The repeater deployment has been designed to support links that are longer than 52 km. This design accommodates deployments on various scales, small, medium, and large, while optimizing resource usage and infrastructure costs. We evaluated the performance and security trade offs in quantum communication networks by optimizing three topologies with a QIGA: ring, ring-star, and ring-star-mesh hybrid (QIGA−OT). The optimization methodology aims to reconcile essential performance metrics, such as the Secure Key Rate (SKR), Quantum Bit Error Rate (QBER), channel loss, and Merit of Quantum Efficiency (MQE). MQE functions as a metric for evaluating trades concerning security, efficiency, and cost-effectiveness within the network. This work presents a novel approach for utilizing quantum-inspired in the effective design and implementation of scalable network topologies across numerous fields of application.
Ensuring network reliability is crucial to support increasing data demands and complex operations. Traditional networks are prone to errors from noise, interference, and environmental factors. This research introduces a hybrid approach that combines Quantum Error Correction (QEC) techniques with Quantum-Inspired Genetic Algorithms (QIGAs) to enhance network reliability. Meta-heuristic algorithms, such as genetic algorithms, efficiently explore large search spaces to find nearoptimal solutions, making them suitable for complex problems. Quantum computing leverages principles like superposition and entanglement, which enhance processing speed and problem-solving capabilities. In this approach, QEC is used to protect data integrity from decoherence and quantum noise, while QIGAs, which emulate natural evolution, enhance QEC performance through efficient solution space exploration. The study highlights the benefits and performance of integrating QEC with QIGAs, with an emphasis on dynamic adjustment for optimal results. Initial simulations show significant improvements in network reliability compared to traditional methods. The hybrid approach demonstrates rapid convergence and effective error correction, proving to be superior to conventional methods. Further investigation is needed to confirm the efficacy of this approach across broader real-world contexts and larger network scales. Nonetheless, this research paves the way for robust and reliable communication systems that can meet the demands of modern data transfer and network complexity. By integrating QEC with QIGAs, we can achieve a new level of network reliability, ensuring that data integrity is maintained even in the face of errors and environmental challenges. This innovative hybrid approach holds promise for the future of network technology, providing a foundation for more reliable and efficient communication systems.
There is an ever-growing need for optimizing efficient nano and quantum communication systems and this demand calls for more advanced optimization techniques than those that have been around for quite some time. The conventional genetic algorithms (GAs) proved to be quite helpful in general but proved to be poor when it comes to fast convergence and adaptability to the changing communication environment. Moreover, fault tolerance and resource efficiency in these communication channels are poorly addressed, giving rise to a huge roadblock to practical implementations in a number of different use cases. This research presents a set of novel hybrid optimization methods aimed at addressing the issues previously mentioned. In the first place, a Quantum-inspired Genetic Algorithm (QIGA) with Simulated Annealing (SA) Initialization is proposed through augmenting traditional genetic operations with the quantum computing paradigm. In the first instance, the hybrid approach works by utilizing the parameters derived from SA while initializing the GA population, hence effective exploration of solution space. Additionally, the Adaptive Genetic Algorithm with Reinforcement Learning (AGA-RL) is presented, which uses real-time feedback from the current environment to modify the GA parameters. This way, the system will be much more adaptive and hence be able to adjust the GA so that it can match the rapidly changing channel conditions. Simultaneously, the Fault-Tolerant Genetic Algorithm with Error-Correcting Codes (FTGA-ECC) is developed to ensure reliable data transmission, especially in an error-prone environment. To make up for resource constraints, the Energy-Aware Genetic Algorithm with Dynamic Resource Allocation (EAGA-DRA) strategy ensures that resources are well-managed by dynamically changing allocation based on the optimization process and the particular needs of the channel, hence reducing energy overhead and spatial overhead without compromising channel quality. A great leap is in the integration of Machine Learning through the Deep Reinforcement Learning-guided Genetic Algorithm (DRL-GA). The above approach exploits deep reinforcement learning to fine-tune the exploration and exploitation strategies of the GA, hence providing faster convergence and improved solution quality. All of these methods are validated by the Quantum-inspired Benchmarking Framework (QIBF), which forms a systematized comparative platform to evaluate different optimization techniques from the perspective of QCA-based communication systems. The above approaches not only provide the improvement in efficiency and reliability but also contribute to enhancing the adaptability of communication systems and provide a very good base for future research and development in the field of quantum communications and beyond, with potential applications extending to secure communication, quantum computing, and nanotechnology-based devices & scenarios. This work is multifold, promising significant advancements in the field of quantum communication and beyond. The empirical findings showed that the suggested algorithms perform significantly better than traditional approaches in several important performance metrics, such as robustness to channel errors, adaptability to changing signal-to-noise ratios, convergence speed under dynamic conditions, and solution quality in highly variable environments.INDEX TERMS Quantum-inspired genetic algorithm, adaptive genetic algorithm, fault-tolerance, energy-efficient optimization, deep reinforcement learning.
This article proposes an optimization model for the design of quantum communication infrastructures (QCI) with the aim of minimizing capital expenditure (CAPEX), focusing on the costs associated with the deployment of quantum links and trusted repeater nodes. The model is formulated using an integer linear programming (ILP) program that incorporates physical and operational constraints to ensure efficient resource allocation. The simulation results obtained using an ILP solver show that increasing wavelength availability leads to significant reductions in CAPEX while consistently producing solutions that satisfy all constraints. However, because ILP becomes computationally expensive for large-scale networks, we propose two genetic algorithms ( GA1 and GA2 ) designed to improve scalability. Both GA1 and GA2 offer competitive computation times compared with the ILP solver and provide near-optimal solutions compared with the ILP solution. Overall, this approach is effective for small, medium, and large-scale QCI deployments, offering a scalable and practical solution suitable for real-world implementation. For example, in the small network topology with nine nodes and nine requests, we observe a reduction of CAPEX up to 33% when the number of available wavelengths increases from 1 to 2, and we note that GAs in a large topology, such as NET-4 network, reduce CAPEX by 34.6% ( GA1 ), and 21.1% ( GA2 ) compared to the benchmarks.
The existence of quantum computing presents an existential threat to the traditional cryptographic algorithms, and there is a need to create communication mechanisms that are fundamentally secure. In this regard, the concept of Quantum Key Distribution (QKD) provides information-theoretic security, which is founded on quantum mechanics. Nevertheless, conventional QKD protocols have serious shortcomings such as lack of distance in communication, vulnerability to side-channel attacks, and inability to support the large-scale heterogeneous network. We suggest that HQNet-QKD a Hybrid Quantum Key Distribution framework can help solve these issues by offering a more secure, scalable and future-proof key exchange framework in hybrid classical-quantum networks. To support multi-layered security, the proposed architecture is a network ensuring the smoothness of decoy-state BB84, Measurement-Device-Independent QKD (MDI-QKD), entanglement/twin-field-based QKD with verifiable Secret Sharing (VSS) and Post-Quantum Cryptography (PQC). It provides a complete methodology such as system architecture, mathematical modeling, and end-to-end key synthesis. When used over a distance of 300 km, simulation and testing have shown that HQNet-QKD can achieve up to2.7×higher secret key rate (SKR) than conventional methods, secret communication at 700 km and resilience to both quantum and classical attacks more than 96%. In addition, the framework guarantees forward secrecy, detector-side immunity, contextbound key derivation, and composability and thus resistant to current and future adversaries. It has a low-computational overhead modular design, making it trivial to deploy in a real world, in applications like quantum-secured 5G backbones, cloud data centers, critical infrastructure, and satellite-based communication systems. The findings make HQNet-QKD a new generation solution to quantum-safe secure communication and the first step towards the implementation of the quantum internet.
The scalability of Quantum Key Distribution (QKD) networks remains a primary challenge for the realization of the quantum internet. Although full mesh topologies offer superior performance, their quadratic scaling of quantum links renders them impractical for large-scale deployment. This paper proposes a novel Hierarchical Quantum Backbone (HQB) topology that addresses this challenge. It is being done by employing a hierarchical architecture composed of a core ring of quantum repeaters interconnected with distribution hubs and trusted nodes serving end-users. A statistical simulation framework incorporating realistic models for channel loss, latency, and quantum bit error rate evaluates the design’s eficiency. The performance evaluation demonstrate that HQB reduces the number of quantum links compared to full-mesh networks. The topology supports near-instantaneous, high-rate intra-hub key distribution as well as metropolitan-scale inter-hub rates. This comprehensive analysis confirms HQB as a practical, efficient, and fault-tolerant architecture suitable for scalable quantum network deployment.
Quantum key distribution (QKD) provides a secure method to exchange encrypted information between two parties in a quantum communication infrastructure (QCI). The primary challenge in deploying a QCI is the cost of using optical fibers and trusted repeater nodes (TRNs). Practical systems combine quantum and classical channels on the same fiber to reduce the cost of fibers dedicated to QKD. In such a system with quantum-classical coexistence, the optimal distribution of QKD requests with minimal deployment cost and power usage on the multiplexed links is challenging due to the diverse key rate demands of the requests, number of classical and quantum channels, guard band spacing between classical and quantum channels, and secret key rate of the quantum channels that decreases with distance. To address these challenges, in this work, we propose a Steiner tree-based approach for constructing a QCI that connects all quantum nodes with minimum TRNs. In addition, we propose a genetic algorithm-based solution to optimally distribute the end-to-end QKD requests over the QCI. We also determine feasible optical bypass routes to reduce the overall energy consumption in the network further. The proposed approach reduces the QCI deployment cost by 19.42% compared to the benchmark MST-Baseline. Also, on average, TAQNet with optical bypass achieves 4.69 kbit per Joule more energy efficiency compared to the nonbypass approach.