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

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

Published on: September 8, 2023

Robust transport over networks.

Yongxin Chen¹, Tryphon Georgiou², Michele Pavon³

¹Department of Mechanical Engineering, University of Minnesota, Minneapolis, Minnesota MN 55455, USA.

IEEE Transactions on Automatic Control

|September 20, 2017

Summary

This summary is machine-generated.

This study introduces a novel method for mass transportation scheduling on networks using Schrödinger bridges, ensuring robust and efficient path utilization. The approach minimizes congestion by favoring uniform path distribution and can be adapted for cost optimization on weighted graphs.

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Area of Science:

Network science
Stochastic processes
Optimization

Background:

Mass transportation scheduling on directed graphs presents challenges in managing flow and constraints.
Existing methods may not adequately address robustness or uniform path utilization.

Purpose of the Study:

To develop a robust and efficient mass transportation scheduling method for directed graphs.
To adapt the Schrödinger bridge paradigm for network transport, considering various graph structures and optimization criteria.

Main Methods:

Utilizing discrete-time Markov evolution with transition probabilities determined by Schrödinger bridges.
Employing a Ruelle-Bowen random walker prior for uniform path distribution and congestion reduction.
Adapting the framework for non-strongly connected and weighted graphs.

Main Results:

The Ruelle-Bowen law (𝔐_RB) as a prior leads to transportation plans that lessen congestion and enhance robustness.
Demonstrated that 𝔐_RB maximizes the entropy rate and can be derived from a maximum entropy problem.
Developed a method for weighted graphs that balances robustness with cost, assigning maximum probability to minimum cost paths.

Conclusions:

Schrödinger bridges offer a powerful framework for network transportation scheduling, adaptable to diverse graph topologies and objectives.
The proposed method provides a robust alternative to traditional strategies like Optimal Mass Transportation, especially for complex networks.