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Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
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On the Baltimore Light RailLink into the quantum future.

Krzysztof Domino1, Emery Doucet2,3, Reece Robertson4,5,6

  • 1Institute of Theoretical and Applied Informatics, Polish Academy of Sciences, Bałtycka 5, 44-100, Gliwice, Poland. kdomino@iitis.pl.

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Summary
This summary is machine-generated.

This study shows how to use quantum noise in noisy intermediate-scale quantum (NISQ) devices as a computational resource for optimization. Researchers applied this to train traffic management, demonstrating a novel approach for complex rescheduling problems.

Keywords:
NISQ deviceQUBO representationQuantum annealingQuantum gate computingStochastic optimizationTramway/railway re-scheduling

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Area of Science:

  • Quantum Computing
  • Operations Research
  • Transportation Systems

Background:

  • Noisy Intermediate-Scale Quantum (NISQ) devices offer potential for complex optimization problems, including NP-hard challenges.
  • Current quantum methods often underperform classical solvers, necessitating innovative approaches.
  • Leveraging inherent quantum noise is an underexplored strategy for enhancing computational power.

Purpose of the Study:

  • To demonstrate the use of quantum noise as a computational resource for optimization problems.
  • To showcase the effective application of NISQ devices for real-world problem-solving.
  • To explore the feasibility of quantum computing for modeling stochastic disruptions in transportation networks.

Main Methods:

  • Utilized a D-Wave quantum annealer and IonQ's gate-based NISQ computers.
  • Generated and analyzed solutions for train traffic management under stochastic disturbances.
  • Focused on the Baltimore Light RailLink as a case study for tramway and railway networks.

Main Results:

  • Successfully applied both quantum computing paradigms (annealing and gate-based) to a real-world transportation rescheduling problem.
  • Demonstrated the potential of leveraging quantum noise for effective optimization.
  • Showcased the feasibility of modeling stochastic disruptions using NISQ technology.

Conclusions:

  • Quantum noise can be effectively leveraged as a computational resource in NISQ devices for optimization.
  • This research represents the first application of both quantum annealing and gate-based NISQ computing to tramway and railway rescheduling.
  • NISQ technology holds promise for addressing complex, real-world optimization challenges in transportation and beyond.