We propose a quantum representation of binary classification trees with binary features based on a probabilistic approach. By using the quantum computer as a processor for probability distributions, a probabilistic traversal of the decision tree can be realized via measurements of a quantum circuit. We describe how tree inductions and the prediction of class labels of query data can be integrated into this framework. An on-demand sampling method enables predictions with a constant number of classical memory slots, independent of the tree depth. We experimentally study our approach using both a quantum computing simulator and actual IBM quantum hardware. To our knowledge, this is the first realization of a decision tree classifier on a quantum device.
翻译:我们根据概率法提出具有二元特征的二元分类树的量子表示法。通过使用量子计算机作为概率分布的处理器,可以通过量子电路测量实现决策树的概率穿行。我们描述了如何将树的感应和对查询数据分类标签的预测纳入这个框架。一个点抽样方法使得能够预测与树深度无关的古典记忆槽数量不变。我们利用量子计算模拟器和实际的IBM量子硬件进行实验性研究。据我们所知,这是首次在量子装置上实现决定树分类。