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Quantum Optimization Quantum Machine Learning

A deceptive step towards quantum speedup detection

arXiv
Authors: Salvatore MandrĂ , Helmut G. Katzgraber

Year

2017

Paper ID

25314

Status

Preprint

Abstract Read

~2 min

Abstract Words

109

Citations

N/A

Abstract

There have been multiple attempts to design synthetic benchmark problems with the goal of detecting quantum speedup in current quantum annealing machines. To date, classical heuristics have consistently outperformed quantum-annealing based approaches. Here we introduce a class of problems based on frustrated cluster loops - deceptive cluster loops - for which all currently known state-of-the-art classical heuristics are outperformed by the D-Wave 2000Q quantum annealing machine. While there is a sizable constant speedup over all known classical heuristics, a noticeable improvement in the scaling remains elusive. These results represent the first steps towards a detection of potential quantum speedup, albeit without a scaling improvement and for synthetic benchmark problems.

Why This Paper Matters

  • This paper contributes to the Quantum Machine Learning research area in the Quantum Articles archive.
  • It adds a 2017 reference point for readers tracking recent quantum research.
  • There have been multiple attempts to design synthetic benchmark problems with the goal of detecting quantum speedup in current quantum annealing machines.

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