# Representation of binary classification trees with binary features by quantum circuits

arXiv: https://arxiv.org/abs/2108.13207 · HTML: https://mlsanity.com/paper/2108.13207/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 87 references that are in the map.
- First posted on arXiv 30 Aug 2021.

## Builds on (most cited first, 17 of 17)

- [Variational Quantum Algorithms](https://mlsanity.com/paper/2012.09265/) (2020)
- [Parameterized quantum circuits as machine learning models](https://mlsanity.com/paper/1906.07682/) (2019)
- [Quantum Machine Learning](https://mlsanity.com/paper/1611.09347/) (2016)
- [Quantum generative adversarial networks](https://mlsanity.com/paper/1804.08641/) (2018)
- [Effect of barren plateaus on gradient-free optimization](https://mlsanity.com/paper/2011.12245/) (2020)
- [Quantum Recommendation Systems](https://mlsanity.com/paper/1603.08675/) (2016)
- [A divide-and-conquer algorithm for quantum state preparation](https://mlsanity.com/paper/2008.01511/) (2020)
- [A Domain-agnostic, Noise-resistant, Hardware-efficient Evolutionary Variational Quantum Eigensolver](https://mlsanity.com/paper/1910.09694/) (2019)
- [Quantum Inference on Bayesian Networks](https://mlsanity.com/paper/1402.7359/) (2014)
- [An Experimental Comparison of Old and New Decision Tree Algorithms](https://mlsanity.com/paper/1911.03054/) (2019)
- [The Quantum Version Of Classification Decision Tree Constructing Algorithm C5.0](https://mlsanity.com/paper/1907.06840/) (2019)
- [Relaxed Peephole Optimization: A Novel Compiler Optimization for Quantum Circuits](https://mlsanity.com/paper/2012.07711/) (2020)
- [Fast Black-Box Quantum State Preparation](https://mlsanity.com/paper/2009.10709/) (2020)
- [Quantum Speedup Based on Classical Decision Trees](https://mlsanity.com/paper/1905.13095/) (2019)
- [Improved Weighted Random Forest for Classification Problems](https://mlsanity.com/paper/2009.00534/) (2020)
- [A Quantum-Inspired Ensemble Method and Quantum-Inspired Forest Regressors](https://mlsanity.com/paper/1711.08117/) (2017)
- [Experimental evaluation of quantum Bayesian networks on IBM QX hardware](https://mlsanity.com/paper/2005.12474/) (2020)

## Built on by (most cited first, 7 of 7)

- [Explaining Quantum Circuits with Shapley Values: Towards Explainable Quantum Machine Learning](https://mlsanity.com/paper/2301.09138/) (2023)
- [Quantum Machine Learning on Near-Term Quantum Devices: Current State of Supervised and Unsupervised Techniques for Real-World Applications](https://mlsanity.com/paper/2307.00908/) (2023)
- [Quantum Neural Network Classifiers: A Tutorial](https://mlsanity.com/paper/2206.02806/) (2022)
- [Des-q: a quantum algorithm to provably speedup retraining of decision trees](https://mlsanity.com/paper/2309.09976/) (2023)
- [Quantum Capsule Networks](https://mlsanity.com/paper/2201.01778/) (2022)
- [QC-Forest: a Classical-Quantum Algorithm to Provably Speedup Retraining of Random Forest](https://mlsanity.com/paper/2406.12008/) (2024)
- [Quantum Random Forest for the Regression Problem](https://mlsanity.com/paper/2603.22790/) (2026)

## Abstract (arXiv)

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.