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Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
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Relaxed Schrödinger bridges and robust network routing.

Yongxin Chen1, Tryphon T Georgiou2, Michele Pavon3

  • 1School of Aerospace Engineering, Georgia Institute of Technology, Atlanta, GA 30332.

IEEE Transactions on Control of Network Systems
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PubMed
Summary
This summary is machine-generated.

We developed a robust network routing method to handle link failures by distributing path choices. This approach ensures network resilience and fast convergence to optimal solutions using a generalized Schrödinger system.

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Area of Science:

  • Network science
  • Optimization theory
  • Applied mathematics

Background:

  • Network routing is critical for data transmission.
  • Random link failures pose a significant challenge to network reliability.
  • Existing routing methods may lack robustness against unpredictable disruptions.

Purpose of the Study:

  • To develop a network routing strategy that is robust to random link failures.
  • To formulate a mathematical approach for achieving resilient network distributions.
  • To ensure network paths can utilize alternatives when links fail.

Main Methods:

  • Formulation of a relaxed transport problem as a maximum entropy problem.
  • Incorporation of an entropic regularizing penalty to encourage path diversity.
  • Mathematical proof showing the solution is obtainable via a generalized Schrödinger system.
  • Development of an iterative algorithm for computing the solution.

Main Results:

  • The proposed method provides a unique solution to the relaxed transport problem.
  • The entropic penalty effectively distributes path choices among alternatives.
  • The iterative algorithm demonstrates rapid convergence, contracting the Hilbert metric distance by over 1/2 per iteration.
  • The solution ensures the final distribution is close to the desired one, mediating link failures.

Conclusions:

  • The generalized Schrödinger system offers an effective way to compute robust network routing solutions.
  • The entropic regularization promotes network resilience by diversifying path selection.
  • The fast convergence of the iterative algorithm makes this method computationally efficient for practical applications.