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Quantum Machine Learning (QML) algorithms to solve classifications problemshave 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 tothe efficacy of such protocols. We evaluate this efficacy using common baselinedata sets, in addition to recent coronavirus spread data as well as the quantummetal-insulator transition in three dimensions. For the computation, we usedthe 16 qubit IBM quantum computer. We find that the "quantum enhancement" isnot generic and fails for more complex machine learning tasks.

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