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Efficient Discrete Feature Encoding for Variational Quantum Classifier

机译：变分量子分类器的高效离散特征编码

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摘要

Recent days have witnessed significant interests in applying quantum-enhanced techniques for solving machine learning tasks in, e.g., classification, regression, and recommender systems. Variational methods that use quantum resources of imperfect quantum devices with the help of classical computing techniques are popular for supervised learning. Variational Quantum Classification (VQC) is one of such variational methods with possible quantum advantage in using quantum-enhanced features that are hard to compute by classical methods. Its performance depends on the mapping of classical features into quantum-enhanced feature space. Although there have been many quantum-mapping functions proposed so far, there is little discussion on efficient mapping of discrete features, such as, race, gender, marriage status and others that are often significant for classifying datasets of interest. We first introduce the use of Quantum Random Access Coding (QRAC) to map such discrete features efficiently into limited number of qubits for VQC. We numerically show that QRAC can help speeding up the training of VQC by reducing its parameters via reduction on the number of qubits for the mapping. We confirm the effectiveness of the QRAC in VQC by experimenting on classification of healthcare datasets with both simulators and real quantum devices.
机译：近年来，目睹了将量子增强技术用于解决例如分类，回归和推荐系统中的机器学习任务的巨大兴趣。在经典计算技术的帮助下，使用不完美量子设备的量子资源的变分方法很受监督学习的欢迎。变分量子分类（VQC）是此类变分方法之一，在使用难以通过经典方法计算的量子增强特征时可能具有量子优势。它的性能取决于经典特征到量子增强特征空间的映射。尽管到目前为止，已经提出了许多量子映射功能，但很少有关于离散特征的有效映射的讨论，例如种族，性别，婚姻状况以及通常对有意义的数据集分类很重要的其他特征。我们首先介绍量子随机访问编码（QRAC）的使用，以将此类离散特征有效地映射到VQC的有限数量的qubit中。我们用数字显示了QRAC可以通过减少映射的qubit数量来减少其参数，从而帮助加快VQC的训练速度。通过使用模拟器和真实量子设备对医疗数据集进行分类实验，我们证实了QRAC在VQC中的有效性。

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