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Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
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Quantum versus simulated annealing in wireless interference network optimization.

Chi Wang1, Huo Chen1, Edmond Jonckheere1

  • 1Department of Electrical Engineering, University of Southern California, Los Angeles, CA90089, USA.

Scientific Reports
|May 17, 2016
PubMed
Summary
This summary is machine-generated.

Quantum annealing (QA) offers advantages over simulated annealing (SA) for wireless network link scheduling. A novel error-reduction technique enhances QA performance, showing potential for real-world applications.

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Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
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Area of Science:

  • Quantum Computing
  • Computer Science
  • Wireless Networking

Background:

  • Quantum annealing (QA) is a specialized optimizer for NP-hard problems, with theoretical advantages over simulated annealing (SA) via quantum tunneling.
  • The D-Wave quantum annealer has spurred research into quantifying quantum speedup, though results remain debated.
  • Existing research often focuses on theoretical speedup, with fewer studies on practical, real-world applications.

Purpose of the Study:

  • To investigate a novel application of D-Wave quantum annealing in wireless networking.
  • To address the challenge of scheduling air-link activation for maximum throughput while avoiding interference near network nodes.
  • To enhance the robustness of D-Wave implementations against errors like spin bias and coupling issues.

Main Methods:

  • Utilizing a D-Wave quantum annealer for a wireless network scheduling problem.
  • Implementing a novel Hamiltonian extra penalty weight adjustment to enlarge the energy gap.
  • Reducing interference violations caused by spin bias and coupling errors through this adjustment.

Main Results:

  • The novel adjustment significantly reduces interference violations, making the D-Wave implementation more error-insensitive.
  • Quantum annealing demonstrated greater benefits than simulated annealing from the gap expansion process.
  • Improvements were observed in both ST99 speedup and network queue occupancy.

Conclusions:

  • The developed error-insensitivity technique benefits quantum annealing more than simulated annealing.
  • This work presents a potential real-world application niche for objectively assessing quantum annealing benefits.
  • The findings suggest quantum annealing can be practically advantageous in complex scheduling problems like wireless networking.