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Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
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Graph coloring using the reduced quantum genetic algorithm.

Sebastian Mihai Ardelean1, Mihai Udrescu1

  • 1Computer and Information Technology, University Politehnica of Timisoara, Timisoara, Timis, Romania.

Peerj. Computer Science
|February 3, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces a Reduced Quantum Genetic Algorithm (RQGA) for solving NP-hard graph coloring problems. Quantum heuristics offer a more efficient approach to complex computational challenges compared to classical methods.

Keywords:
Graph coloringQuantum computingQuantum genetic algorithms

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Area of Science:

  • Quantum computing
  • Computational complexity
  • Heuristic algorithms

Background:

  • Genetic algorithms (GA) are natural selection-inspired computational methods for optimization.
  • Analyzing GA in quantum contexts is feasible through simulations and actual quantum hardware.
  • NP-hard problems present significant computational challenges for classical algorithms.

Purpose of the Study:

  • To propose and analyze a Reduced Quantum Genetic Algorithm (RQGA) for NP-hard problems.
  • To apply RQGA to the graph coloring problem, including vertex and edge coloring.
  • To determine the chromatic number of graphs using a quantum approach.

Main Methods:

  • Implementation of a Reduced Quantum Genetic Algorithm (RQGA) circuit.
  • Utilizing the Qiskit simulation environment for analysis.
  • Solving graph coloring problems, including vertex and edge coloring, and determining the chromatic number.

Main Results:

  • The RQGA solves the graph coloring problem with a time complexity of O(N^1/2).
  • The algorithm successfully determines the chromatic number (minimum colors required).
  • Performance analysis and convergence were measured within the Qiskit simulation environment.

Conclusions:

  • Quantum heuristics, specifically RQGA, can efficiently address complex computational problems.
  • The proposed RQGA demonstrates superior efficiency for graph coloring compared to conventional methods.
  • Quantum computing offers a promising avenue for tackling NP-hard problems more effectively.