Selected article for: "addition common and machine learning"

Author: McRae, Paul-Aymeric; Hilke, Michael
Title: Quantum-Enhanced Machine Learning for Covid-19 and Anderson Insulator Predictions
  • Cord-id: 8rzgsomv
  • Document date: 2020_12_7
  • ID: 8rzgsomv
    Snippet: 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
    Document: 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.

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