A quantum network connecting quantum devices is expected to enhance quantum computing and information processing by increasing the number of qubits, and distributing computation and processing. However, noise-induced decoherence remains a big challenge as a quantum network can experience multiple sources of noises. Optimizing a quantum network for a given noise regime thus can improve its performance and reliability. This research uses multi-objective optimization with a genetic algorithm to find optimal conditions for constructing a quantum network in specific configurations. We employed the qwanta quantum network simulator to investigate two scenarios of a three-node quantum network with a quantum repeater in the middle. Pareto frontier analysis enables us to visualize the trade-offs between a quantum state fidelity and throughput in a quantum network. The results provide parameters that achieve the fidelity higher than the targeted value of 0.828 for quantum key distribution in a linear network with equal distances between nodes. However, for a three-nodes network with unequal distances based on Thailand's geographical locations, the highest fidelity achieved is 0.706. Expanding a genetic algorithm's search space to include a broader parameter range can potentially improve the results. This research demonstrates potential deployment of a genetic algorithm with multiple-objective optimization in a quantum network.
A quantum network connecting quantum devices is expected to enhance quantum computing and information processing by increasing the number of qubits, and distributing computation and processing. However, noise-induced decoherence remains a big challenge as a quantum network can experience multiple sources of noises. Optimizing a quantum network for a given noise regime thus can improve its performance and reliability. This research uses multi-objective optimization with a genetic algorithm to find optimal conditions for constructing a quantum network in specific configurations. We employed the qwanta quantum network simulator to investigate two scenarios of a three-node quantum network with a quantum repeater in the middle. Pareto frontier analysis enables us to visualize the trade-offs between a quantum state fidelity and throughput in a quantum network. The results provide parameters that achieve the fidelity higher than the targeted value of 0.828 for quantum key distribution in a linear network with equal distances between nodes. However, for a three-nodes network with unequal distances based on Thailand's geographical locations, the highest fidelity achieved is 0.706. Expanding a genetic algorithm's search space to include a broader parameter range can potentially improve the results. This research demonstrates potential deployment of a genetic algorithm with multiple-objective optimization in a quantum network.
Multi‐objective optimization in quantum systems poses formidable challenges stemming from the high dimensionality, complex constraints, and conflicting performance metrics inherent to quantum control tasks and circuit synthesis. This study presents a comprehensive exploration of genetic AI algorithms—evolutionary strategies inspired by natural selection—tailored specifically for multi‐objective optimization in quantum computing contexts. We propose a hybrid framework that synergistically combines the elitist, Pareto‐based selection mechanics of NSGA-II with quantum‐inspired crossover and mutation operators designed to preserve entanglement structures and exploit hardware‐specific gate sets. In this framework, candidate solutions encoding circuit topologies or control‐pulse parameters undergo iterative evolution, guided by fitness vectors that balance gate fidelity, circuit depth, and resource overhead. To benchmark performance, we apply the algorithm to canonical quantum computing problems: Fourier transform circuits, QAOA ansätze for MaxCut instances, and random Clifford+T circuits. Through extensive simulation on noise-modelled quantum devices, we evaluate Pareto front coverage, convergence speed, diversity maintenance, and robustness under decoherence. Our results demonstrate that the proposed genetic AI algorithm outperforms classical single‐objective genetic methods and established multi‐objective evolutionary algorithms (e.g., SPEA2) by achieving broader Pareto fronts and faster convergence rates, while maintaining solution diversity. Furthermore, noise-resilience experiments show graceful degradation, indicating practical applicability on near-term quantum hardware. These findings substantiate the potential of evolutionary computation paradigms—augmented with quantum‐inspired operators—to address the intricate trade-off surfaces that define NISQ-era optimization problems, paving the way toward automated quantum circuit design and control parameter tuning for real-world quantum computing applications.
At first, the entanglement source deployment problem is studied in a quantum multi-hop network, which has a significant influence on quantum connectivity. Two optimization algorithms are introduced with limited entanglement sources in this paper. A deployment algorithm based on node position (DNP) improves connectivity by guaranteeing that all overlapping areas of the distribution ranges of the entanglement sources contain nodes. In addition, a deployment algorithm based on an improved genetic algorithm (DIGA) is implemented by dividing the region into grids. From the simulation results, DNP and DIGA improve quantum connectivity by 213.73% and 248.83% compared to random deployment, respectively, and the latter performs better in terms of connectivity. However, DNP is more flexible and adaptive to change, as it stops running when all nodes are covered.
Quantum networks facilitate communication among quantum devices, playing a crucial role in enabling applications beyond the reach of classical networks. However, creating a quantum network that supports reliable long-distance entanglement distribution remains a significant challenge. Unlike classical networks, connections in quantum networks are established through entanglement generation. Effective communication between quantum devices requires a routing strategy that efficiently manages the establishment of entanglement. In addition to fidelity, the quality of communication is also determined by the capacity of the generated entanglement. Therefore, we proposed a capacity allocation scheduling scheme based on a metaheuristic approach, namely a Genetic Algorithm (GA), to ensure efficient capacity allocation for multiple communication paths within each communication request. Simulations were conducted in four different scenarios, and the results were compared to an existing capacity allocation scheduling algorithm, Progressive Filling (PF), to verify the effectiveness of the proposed scheme. The average capacity utilisation, U and weighted throughput, F results indicate that PF outperforms GA in a scenario with four communication requests, while GA performs better in scenarios involving six, eight, and ten communication requests. Specifically, the improvement in average capacity utilisation ranges from 8% to 46%, and the improvement in weighted throughput ranges from 24% to 94%. These results suggest that GA can serve as a valuable reference for designing optimal routing and capacity allocation schemes in more complex networks with multiple communication requests.
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.
Quantum networks offer the capability of transferring quantum information among remote nodes leveraging the phenomenon of quantum entanglement. A key challenge is entanglement routing, which routes quantum entanglement efficiently in a quantum network to satisfy specific objectives. We focus on an interesting hypothesis that arises from the probabilistic nature of quantum entanglement: There is a tradeoff between accommodating a higher number of quantum entanglements (performance) and serving a broader range of node pairs (fairness). To confirm this hypothesis, we formulate entanglement routing as a multi-objective optimization problem. Our numerical analysis reveals that the solutions of the model form a Pareto frontier, thereby validating the hypothesis. Furthermore, we compare the performance of existing algorithms against this Pareto frontier to understand how the algorithms balance performance and fairness. Our findings contribute to designing entanglement routing algorithms.