Hierarchical quantum classifiers
Hierarchical quantum classifiers
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DOI：
10.1038/s41534-018-0116-9
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发表时间：
2018-12-17
影响因子：
7.6
通讯作者：
Severini, Simone
中科院分区：
文献类型：
--
作者：
Grant, Edward;Benedetti, Marcello;Severini, Simone
具有分级结构的量子电路已经被用于执行以量子状态编码的经典数据的二进制分类。我们证明，在同一个家庭中，更有表现力的电路实现更好的准确性，并可用于分类高度纠缠的量子态，没有已知的有效的经典方法。我们在两个经典的机器学习数据集Iris和MNIST以及量子态的合成数据集上比较了几种不同参数化的性能。最后，我们证明了性能对噪声具有鲁棒性，并在ibmqx4量子计算机上部署了Iris数据集分类器。
Quantum circuits with hierarchical structure have been used to perform binary classification of classical data encoded in a quantum state. We demonstrate that more expressive circuits in the same family achieve better accuracy and can be used to classify highly entangled quantum states, for which there is no known efficient classical method. We compare performance for several different parameterizations on two classical machine learning datasets, Iris and MNIST, and on a synthetic dataset of quantum states. Finally, we demonstrate that performance is robust to noise and deploy an Iris dataset classifier on the ibmqx4 quantum computer.