Multi-Objective Routing Optimization for 6G Communication Networks Using a Quantum Approximate Optimization Algorithm

Sixth-generation wireless (6G) technology has been focused on in the wireless research community. Global coverage, massive spectrum usage, complex new applications, and strong security are among the new paradigms introduced by 6G. However, realizing such features may require computation capabilities...

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Bibliographic Details
Published in:Sensors (Basel, Switzerland) Vol. 22; no. 19; p. 7570
Main Authors: Urgelles, Helen, Picazo-Martinez, Pablo, Garcia-Roger, David, Monserrat, Jose F.
Format: Journal Article
Language:English
Published: Basel MDPI AG 01.10.2022
MDPI
Subjects:
6G communication networks
Algorithms
Atoms
Circuits
Communications networks
Computers
Internet of Things
Linear programming
Machine learning
Mathematical optimization
multi-objective
Optimization algorithms
Optimization techniques
Quantum computing
quantum optimization algorithms
Quantum physics
quantum routing optimization
Traveling salesman problem
Wireless communications
ISSN:1424-8220, 1424-8220
Online Access:Get full text
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Summary:Sixth-generation wireless (6G) technology has been focused on in the wireless research community. Global coverage, massive spectrum usage, complex new applications, and strong security are among the new paradigms introduced by 6G. However, realizing such features may require computation capabilities transcending those of present (classical) computers. Large technology companies are already exploring quantum computers, which could be adopted as potential technological enablers for 6G. This is a promising avenue to explore because quantum computers exploit the properties of quantum states to perform certain computations significantly faster than classical computers. This paper focuses on routing optimization in wireless mesh networks using quantum computers, explicitly applying the quantum approximate optimization algorithm (QAOA). Single-objective and multi-objective examples are presented as robust candidates for the application of quantum machine learning. Moreover, a discussion about quantum supremacy estimation for this problem is provided.
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ISSN:1424-8220
1424-8220
DOI:10.3390/s22197570