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

Quantum Machine Learning (QML) algorithms to solve classifications problems have been made available thanks to recent advancements in 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 metal-insulator transition in three dimensions. For the computation, we used the 16 qubit IBM computer. We find that the quantum enhancement is not generic and fails for more complex machine learning tasks.