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Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
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Quantum-enhanced greedy combinatorial optimization solver.

Maxime Dupont1, Bram Evert1, Mark J Hodson1

  • 1Rigetti Computing, Berkeley, CA 94710, USA.

Science Advances
|November 10, 2023
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Summary
This summary is machine-generated.

A new quantum algorithm for combinatorial optimization shows quantum enhancement over classical methods. Implemented on a 72-qubit system, it offers performance competitive with state-of-the-art techniques, despite hardware noise challenges.

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Area of Science:

  • Quantum computing
  • Combinatorial optimization
  • Quantum algorithms

Background:

  • Quantum advantage in combinatorial optimization remains elusive.
  • Quantum hardware noise presents a significant challenge.
  • Sophisticated quantum-classical algorithms are needed.

Purpose of the Study:

  • Introduce an iterative quantum heuristic optimization algorithm.
  • Address combinatorial optimization problems.
  • Investigate quantum enhancement in noisy hardware.

Main Methods:

  • Developed an iterative quantum heuristic optimization algorithm.
  • Implemented the algorithm on a 72-qubit superconducting quantum system.
  • Tested on Sherrington-Kirkpatrick Ising spin glass problems.

Main Results:

  • The quantum algorithm outperformed its classical greedy counterpart.
  • Observed quantum enhancement in solving optimization problems.
  • Achieved performance comparable to state-of-the-art semidefinite programming.

Conclusions:

  • The developed quantum algorithm demonstrates a quantum enhancement.
  • Improving quantum device characteristics is key to achieving full quantum advantage.
  • The algorithm shows promise for tackling complex optimization tasks.