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arXiv 2012.03472quant-phcond-mat.dis-nn
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Quantum-Enhanced Machine Learning for Covid-19 and Anderson Insulator Predictions

Paul-Aymeric McRae, Michael Hilke

英文摘要:

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.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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