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

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Improvement of Quantum Approximate Optimization Algorithm for Max-Cut Problems.

Javier Villalba-Diez^1,2, Ana González-Marcos³, Joaquín B Ordieres-Meré⁴

¹Hochschule Heilbronn, Fakultät Management und Vertrieb, Campus Schwäbisch Hall, 74523 Schwäbisch Hall, Germany.

Sensors (Basel, Switzerland)

|January 11, 2022

Summary

This study optimizes network partitioning to maximize interconnections, enhancing system resilience. We improve the quantum approximate optimization algorithm (QAOA) for efficient Max-Cut problem solutions.

Keywords:

Industry 4.0 optimization quantum approximate optimization algorithm value–stream networks

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Area of Science:

Network science
Quantum computing
Optimization algorithms

Background:

Value stream networks are critical in systems like communication networks and industrial applications.
Network topology significantly impacts system resilience.
Existing Max-Cut algorithms, including those in quantum approximate optimization, have room for improvement.

Purpose of the Study:

To determine the optimal partitioning of value stream networks into two classes.
To maximize the number of connections between these two partitions.
To enhance the efficiency of the Max-Cut algorithm within the quantum approximate optimization approach (QAOA).

Main Methods:

Investigating network partitioning strategies.
Applying and refining the quantum approximate optimization algorithm (QAOA).
Analyzing Max-Cut algorithm performance for network optimization.

Main Results:

Identified optimal partitioning methods for value stream networks.
Achieved a more efficient implementation of the Max-Cut algorithm using QAOA.
Demonstrated potential for improved network resilience through optimized partitioning.

Conclusions:

Optimal network partitioning is key to maximizing interconnections and enhancing resilience.
The refined QAOA approach offers a more efficient solution for the Max-Cut problem.
Further research can explore advanced applications and variations of this optimization technique.