We propose a hybrid quantum-classical computing mechanism with Coherent Ising Machine for dynamic QoT-aware RMSA in MB-FONs. The solution time is reduced from nearly 2 sec to 323 μs compared with the auxiliary graph method.
The exponential growth of global network traffic has made the expansion of network capacity increasingly urgent. Multi-band and flexible grid technologies have emerged as promising solutions to enhance network transmission capacity. Fast routing is an important measure to ensure the survivability of a multi-band flexible optical network (MB-FON) that carries massive services. However, the extensive network resources and the inter-channel stimulated Raman scattering (ISRS) effect between different bands pose a challenge to the efficient solution of classical routing, modulation, and spectrum allocation (RMSA) methods. This paper leverages the parallel computing advantage of quantum computing and proposes a hybrid quantum-classical computing mechanism for quality of transmission (QoT)-aware dynamic RMSA in MB-FON. In this mechanism, a decouple-then-integrate strategy is proposed to formulate the QoT-aware RMSA problem as a quadratic unconstrained binary optimization model. The augmented Lagrangian method is introduced to deal with the inequality constraint in an ISRS-aware formulation, and a path fragmentation metric is proposed to design the optimization objective. In addition, a coherent Ising machine is used to solve the problem by adjusting the parameters and precision in combination with classical computing methods. Experiments were conducted on network topologies of different sizes. The experimental results show that the solution time can be reduced from nearly 6 s to 537 µs compared with the auxiliary graph-based heuristic algorithm in a 142-node network. It also demonstrates that the proposed mechanism outperforms classical heuristic algorithms in terms of blocking probability and spectrum utilization.
The rapid advancement of beyond-5G and 6G services is creating computational challenges that classical optimisation methods for Elastic Optical Networks (EONs) cannot effectively handle. Specifically, the multi-objective Routing and Spectrum Assignment (RSA) problem—aimed at minimising blocking probability, maximising spectral efficiency, and reducing fragmentation—poses significant challenges and is NP-hard, particularly in dynamic traffic. This paper introduces a hybrid framework that combines quantum and classical computing, dividing the optimisation tasks into classical pre-processing, a quantum optimisation core, and classical post-processing with Pareto frontier management. The RSA problem is modelled using a Quadratic Unconstrained Binary Optimisation (QUBO) formulation that accounts for blocking, efficiency, and a quadratic fragmentation metric. Simulations conducted on NSFNET and UBN topologies under Poisson traffic conditions revealed that even in realistic, noisy quantum environments, this hybrid method reduces the blocking probability by 14% and improves fragmentation by 7.3% compared to the top classical heuristics. A scaling analysis indicates a key point of around 220 variables where this hybrid strategy surpasses traditional meta-heuristics in both solution quality and execution time, emphasising its significant potential in the current NISQ era.
Cloud computing has transformed modern digital infrastructure by enabling on-demand access to scalable computational resources. However, the increasing complexity of dynamic workloads and the need for efficient resource allocation present persistent challenges in maintaining performance, cost efficiency, and energy sustainability. This paper introduces a Quantum-Driven Optimization (QDO) framework, a hybrid quantum-classical approach designed to enhance cloud resource allocation by integrating quantum computing techniques with classical optimization methods. The proposed framework leverages Quantum Annealing (QA) and the Variational Quantum Eigensolver (VQE) to optimize resource distribution, minimizing energy consumption and operational costs while maximizing throughput and utilization efficiency. Experimental evaluations demonstrate that the QDO framework achieves a 27% improvement in resource utilization, a 34% reduction in operational costs, and a 21% enhancement in task completion time compared to traditional heuristic-based approaches. Additionally, the hybrid model reduces Service Level Agreement (SLA) violations by 18%, ensuring robust Quality of Service (QoS) for cloud users. The framework employs classical algorithms for preprocessing and decision-making while delegating complex optimization tasks to quantum solvers, ensuring scalability across diverse cloud environments. This study highlights the transformative potential of hybrid quantum-classical computing in addressing cloud resource allocation challenges. The results indicate significant improvements in energy efficiency, cost-effectiveness, and system responsiveness, making the QDO framework a viable solution for next-generation cloud infrastructures. Future research directions include extending the framework to multi-cloud architectures and investigating advanced quantum algorithms for further optimization gains.
The widespread deployment of 5G networks has drawn growing attention to the optimization of wireless communication systems. Among various techniques, quantum-based approaches are distinguished by their remarkably fast computation time compared to conventional optimization methods. In this paper, we propose a quantum-inspired approach to the wireless resource allocation problem. The problem is formulated as an Ising model and solved using both Coherent Ising Machine (CIM) and D-Wave’s quantum annealer. Furthermore, the performance of these quantum-based methods is compared with that of the Simulated Annealing (SA) algorithm.
Rapid advancements in cloud based platforms providing access to quantum computing capabilities have opened up several challenges for efficient usage of these highly delicate and costly devices. Although most of the current systems use a priority based access protocol, they are unable to fully support reliable, efficient, and scalable execution of larger-scale applications. To overcome this limitation, we propose a comprehensive solution for efficient allocation of quantum programs to appropriate quantum devices, considering all the relevant cost metrics into account including, fidelity, execution time and communication overhead. We also formulate use-cases for distributed quantum workflow and propose modified graph based algorithms to solve for allocation of such use-cases, assuming a hybrid classical-quantum network. Since hardware advancements in large standalone devices is an ongoing process, it is critical to investigate such distributed workflows to maximize the best utilization of current NISQ devices. Our empirical study shows that the proposed techniques perform better than state-of-the-art methods for almost all evaluation parameters, with average improvements of approximately 5% in execution time, 30% in communication overhead, 40% in wait time and 2% in fidelity, providing better solutions to efficient allocation strategies.
As a prototype of quantum communications, quantum key distribution (QKD) is recommended for integration into the existing optical infrastructures to enable scalable quantum networking. This necessitates the development of supporting resource allocation strategies for carrier-grade operations. In this work, we propose a quantum-aware RWA framework with dual-granularity power control (QRA-PC) to mitigate quantum-classical interference under hybrid network constraints. In a 9-node mesh network with 1 GHz discrete-variable QKD systems coexisting in the C-band, QRA-PC with local power control mode achieves an average SKR of 891.9 kb/s at a traffic load of 50 erlangs, compared to the first-fit baseline of 50.4 kb/s. This result is achieved without incurring any classical traffic blocking.