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Trapped Ion Quantum Computing Quantum Machine Learning

Quantum-Enhanced Machine Learning for Covid-19 and Anderson Insulator Predictions

arXiv
Authors: Paul-Aymeric McRae, Michael Hilke

Year

2020

Paper ID

18680

Status

Preprint

Abstract Read

~2 min

Abstract Words

103

Citations

N/A

Abstract

Quantum Machine Learning (QML) algorithms to solve classifications problems have been made available thanks to recent advancements in quantum computation. While the number of qubits are still relatively small, they have been used for "quantum enhancement" of machine learning. An important question is related to the efficacy of such protocols. We evaluate this efficacy using common baseline data sets, in addition to recent coronavirus spread data as well as the quantum metal-insulator transition in three dimensions. For the computation, we used the 16 qubit IBM quantum computer. We find that the "quantum enhancement" is not generic and fails for more complex machine learning tasks.

Why This Paper Matters

  • This paper contributes to the Quantum Machine Learning research area in the Quantum Articles archive.
  • It adds a 2020 reference point for readers tracking recent quantum research.
  • Quantum Machine Learning (QML) algorithms to solve classifications problems have been made available thanks to recent advancements in quantum computation.

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References & Citation Signals

[1] DOI https://doi.org/arXiv:2012.03472 [2] arXiv https://arxiv.org/abs/2012.03472 [3] Publisher https://arxiv.org/abs/2012.03472

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