This paper proposes an extension of regression trees by quadratic unconstrained binary optimization (QUBO). Regression trees are very popular prediction models that are trainable with tabular datasets, but their accuracy is insufficient because the decision rules are too simple. The proposed method extends the decision rules in decision trees to multi-dimensional boundaries. Such an extension is generally unimplementable because of computational limitations, however, the proposed method transforms the training process to QUBO, which enables an annealing machine to solve this problem.

翻译：本文提出了一种通过二次无约束二元优化（QUBO）扩展回归树的方法。回归树是非常流行的预测模型，可基于表格数据集进行训练，但其精度不足，原因在于决策规则过于简单。所提方法将决策树中的决策规则扩展为多维边界。由于计算限制，此类扩展通常无法实现，然而，所提方法将训练过程转化为QUBO形式，从而使退火机能够解决该问题。