# A Quantum-Inspired Ensemble Method and Quantum-Inspired Forest Regressors

arXiv: https://arxiv.org/abs/1711.08117 · HTML: https://mlsanity.com/paper/1711.08117/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Nov 2017.

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

- [Representation of binary classification trees with binary features by quantum circuits](https://mlsanity.com/paper/2108.13207/) (2021)
- [A Quantum Bagging Algorithm with Unsupervised Base Learners for Label Corrupted Datasets](https://mlsanity.com/paper/2509.07040/) (2025)

## Abstract (arXiv)

We propose a Quantum-Inspired Subspace(QIS) Ensemble Method for generating feature ensembles based on feature selections. We assign each principal component a Fraction Transition Probability as its probability weight based on Principal Component Analysis and quantum interpretations. In order to generate the feature subset for each base regressor, we select a feature subset from principal components based on Fraction Transition Probabilities. The idea originating from quantum mechanics can encourage ensemble diversity and the accuracy simultaneously. We incorporate Quantum-Inspired Subspace Method into Random Forest and propose Quantum-Inspired Forest. We theoretically prove that the quantum interpretation corresponds to the first order approximation of ensemble regression. We also evaluate the empirical performance of Quantum-Inspired Forest and Random Forest in multiple hyperparameter settings. Quantum-Inspired Forest proves the significant robustness of the default hyperparameters on most data sets. The contribution of this work is two-fold, a novel ensemble regression algorithm inspired by quantum mechanics and the theoretical connection between quantum interpretations and machine learning algorithms.