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Updated: Jun 6, 2026

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
05:30

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Published on: September 8, 2023

Multi-objective node placement optimization in multiplex 6G wireless networks using quantum-inspired evolutionary

Dhananjai Vs1, Sathi Sailesh Reddy1, K Abhimanyu Kumar Patro2

  • 1Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, India.

Scientific Reports
|June 4, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces a novel quantum-inspired evolutionary learning framework for optimizing node placement in sixth-generation (6G) wireless networks. The proposed method enhances network deployment fitness and convergence speed compared to existing algorithms.

Keywords:
6G wireless networksMulti-objective optimizationMultiplex networksNode placement optimizationQuantum-inspired evolutionary learning

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Last Updated: Jun 6, 2026

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
05:30

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Published on: September 8, 2023

Area of Science:

  • Computer Science
  • Electrical Engineering
  • Network Engineering

Background:

  • Sixth-generation (6G) wireless networks require advanced node placement algorithms to manage complex, heterogeneous deployments.
  • Existing single-layer graph models and traditional optimization methods fail to capture the interdependent nature of 6G networks.

Purpose of the Study:

  • To develop a multi-objective optimization framework for efficient node placement in multiplex 6G wireless networks.
  • To address challenges of high connectivity density and competing performance requirements in 6G network planning.

Main Methods:

  • A multiplex graph representation models interactions between capacity, latency, and interference layers.
  • Quantum-inspired representations facilitate global exploration and prevent premature convergence.
  • A composite fitness expression optimizes network capacity, incentive-aware participation, and multi-layer node centrality.

Main Results:

  • The proposed Quantum-Inspired Evolutionary Algorithm (QIEA) framework achieves a balanced fitness score of 3.0355 and a high node cooperation rate.
  • QIEA improves overall deployment fitness by significant margins over random selection and greedy degree-based placement.
  • The framework demonstrates [Formula: see text] faster convergence than standard Genetic Algorithms (GA).

Conclusions:

  • The QIEA framework offers a superior, scalable solution for large-scale 6G network optimization.
  • This approach effectively handles the complexities of interdependent layers in future wireless networks.
  • The study highlights the potential of quantum-inspired learning for advancing 6G network design.