# QUBO Decision Tree: Annealing Machine Extends Decision Tree Splitting

arXiv: https://arxiv.org/abs/2303.09772 · HTML: https://mlsanity.com/paper/2303.09772/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 66 references that are in the map.
- First posted on arXiv 17 Mar 2023.

## Builds on (most cited first, 15 of 15)

- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [Wide & Deep Learning for Recommender Systems](https://mlsanity.com/paper/1606.07792/) (2016)
- [Consistent Individualized Feature Attribution for Tree Ensembles](https://mlsanity.com/paper/1802.03888/) (2018)
- [Scaling Hamiltonian Monte Carlo Inference for Bayesian Neural Networks with Symmetric Splitting](https://mlsanity.com/paper/2010.06772/) (2020)
- [QUBO Formulations for Training Machine Learning Models](https://mlsanity.com/paper/2008.02369/) (2020)
- [Quantum Permutation Synchronization](https://mlsanity.com/paper/2101.07755/) (2021)
- [A Quantum Computational Approach to Correspondence Problems on Point Sets](https://mlsanity.com/paper/1912.12296/) (2019)
- [Quantum-soft QUBO Suppression for Accurate Object Detection](https://mlsanity.com/paper/2007.13992/) (2020)
- [Quantum Annealing Formulation for Binary Neural Networks](https://mlsanity.com/paper/2107.02751/) (2021)
- [Quantum Annealing Applied to De-Conflicting Optimal Trajectories for Air Traffic Management](https://mlsanity.com/paper/1711.04889/) (2017)
- [Matching Point Sets with Quantum Circuit Learning](https://mlsanity.com/paper/2102.06697/) (2021)
- [A Scalable Two Stage Approach to Computing Optimal Decision Sets](https://mlsanity.com/paper/2102.01904/) (2021)
- [Bayesian Network Structure Learning Using Quantum Annealing](https://mlsanity.com/paper/1407.3897/) (2014)
- [Quantum Annealing for Clustering](https://mlsanity.com/paper/1408.2035/) (2014)
- [Performance of a Quantum Annealer for Ising Ground State Computations on Chimera Graphs](https://mlsanity.com/paper/1904.11965/) (2019)

## Built on by (most cited first, 2 of 2)

- [Hybrid Heuristic Algorithms for Adiabatic Quantum Machine Learning Models](https://mlsanity.com/paper/2407.21062/) (2024)
- [Quadratic Unconstrained Binary Optimisation for Training and Regularisation of Binary Neural Networks](https://mlsanity.com/paper/2601.00449/) (2026)

## Abstract (arXiv)

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.