Quantum-Inspired Network Optimization in 6G: Opportunities, Challenges and Open Research Directions

Bouchmal, Oumayma; Cimoli, Bruno; Olmos, Juan José Vegas; Monroy, Idelfonso Tafur

doi:10.1007/978-3-031-38318-2_48

Cited by 2 publications

(1 citation statement)

References 26 publications

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“…Moreover, the Multi-Agent RL (MARL) approach can be also adopted. This method involves multiple agents learning and making decisions simultaneously, potentially optimizing response times and decision-making processes (Zhang et al, 2021) (Dong et al, 2008;Bouchmal et al, 2023). • Combining SDN and QML: An interesting future research direction that can fulfill 6G routing requirements such as the on-demand configuration, high-dynamic and heterogeneous networks, real-time routing decisions and high QoS obligations could be combining the flexibility and automation given by SDN and the power provided by QML algorithms.…”

Section: Open Research Directionsmentioning

confidence: 99%

From classical to quantum machine learning: survey on routing optimization in 6G software defined networking

Bouchmal,

Cimoli,

Stabile

et al. 2023

Front. Comms. Net.

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The sixth generation (6G) of mobile networks will adopt on-demand self-reconfiguration to fulfill simultaneously stringent key performance indicators and overall optimization of usage of network resources. Such dynamic and flexible network management is made possible by Software Defined Networking (SDN) with a global view of the network, centralized control, and adaptable forwarding rules. Because of the complexity of 6G networks, Artificial Intelligence and its integration with SDN and Quantum Computing are considered prospective solutions to hard problems such as optimized routing in highly dynamic and complex networks. The main contribution of this survey is to present an in-depth study and analysis of recent research on the application of Reinforcement Learning (RL), Deep Reinforcement Learning (DRL), and Quantum Machine Learning (QML) techniques to address SDN routing challenges in 6G networks. Furthermore, the paper identifies and discusses open research questions in this domain. In summary, we conclude that there is a significant shift toward employing RL/DRL-based routing strategies in SDN networks, particularly over the past 3 years. Moreover, there is a huge interest in integrating QML techniques to tackle the complexity of routing in 6G networks. However, considerable work remains to be done in both approaches in order to accomplish thorough comparisons and synergies among various approaches and conduct meaningful evaluations using open datasets and different topologies.

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Section: Open Research Directionsmentioning

confidence: 99%

From classical to quantum machine learning: survey on routing optimization in 6G software defined networking

Bouchmal,

Cimoli,

Stabile

et al. 2023

Front. Comms. Net.

View full text Add to dashboard Cite

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Novel Application of Quantum Computing for Routing and Spectrum Assignment in Flexi-Grid Optical Networks

Bouchmal,

Cimoli,

Stabile

et al. 2024

Photonics

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Flexi-grid technology has revolutionized optical networking by enabling Elastic Optical Networks (EONs) that offer greater flexibility and dynamism compared to traditional fixed-grid systems. As data traffic continues to grow exponentially, the need for efficient and scalable solutions to the routing and spectrum assignment (RSA) problem in EONs becomes increasingly critical. The RSA problem, being NP-Hard, requires solutions that can simultaneously address both spatial routing and spectrum allocation. This paper proposes a novel quantum-based approach to solving the RSA problem. By formulating the problem as a Quadratic Unconstrained Binary Optimization (QUBO) model, we employ the Quantum Approximate Optimization Algorithm (QAOA) to effectively solve it. Our approach is specifically designed to minimize end-to-end delay while satisfying the continuity and contiguity constraints of frequency slots. Simulations conducted using the Qiskit framework and IBM-QASM simulator validate the effectiveness of our method. We applied the QAOA-based RSA approach to small network topology, where the number of nodes and frequency slots was constrained by the limited qubit count on current quantum simulator. In this small network, the algorithm successfully converged to an optimal solution in less than 30 iterations, with a total runtime of approximately 10.7 s with an accuracy of 78.8%. Additionally, we conducted a comparative analysis between QAOA, integer linear programming, and deep reinforcement learning methods to evaluate the performance of the quantum-based approach relative to classical techniques. This work lays the foundation for future exploration of quantum computing in solving large-scale RSA problems in EONs, with the prospect of achieving quantum advantage as quantum technology continues to advance.

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Quantum-Inspired Network Optimization in 6G: Opportunities, Challenges and Open Research Directions

Cited by 2 publications

References 26 publications

From classical to quantum machine learning: survey on routing optimization in 6G software defined networking

From classical to quantum machine learning: survey on routing optimization in 6G software defined networking

Novel Application of Quantum Computing for Routing and Spectrum Assignment in Flexi-Grid Optical Networks

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