论文标题
COVID-19和Anderson绝缘体预测的量子增强机器学习
Quantum-Enhanced Machine Learning for Covid-19 and Anderson Insulator Predictions
论文作者
论文摘要
量子机学习（QML）算法以解决分类问题，这要归功于量子计算的最新进展。尽管量子位数量仍然相对较少，但它们已用于机器学习的“量子增强”。一个重要的问题与此类协议的功效有关。除了最近的冠状病毒扩散数据以及三个维度的量子金属绝缘体过渡外，我们还使用常见的基线数据集评估了这种功效。为了进行计算，我们使用了16个Qubit IBM量子计算机。我们发现“量子增强”不是通用的，并且对于更复杂的机器学习任务而失败。
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.
