# Quantum Ensemble for Classification

arXiv: https://arxiv.org/abs/2007.01028 · HTML: https://mlsanity.com/paper/2007.01028/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Jul 2020.

## Built on by (most cited first, 11 of 11)

- [Mixed Quantum-Classical Method For Fraud Detection with Quantum Feature Selection](https://mlsanity.com/paper/2208.07963/) (2022)
- [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)
- [Classical ensemble of Quantum-classical ML algorithms for Phishing detection in Ethereum transaction networks](https://mlsanity.com/paper/2211.00004/) (2022)
- [MAQA: A Quantum Framework for Supervised Learning](https://mlsanity.com/paper/2303.11028/) (2023)
- [Quantum Supervised Learning](https://mlsanity.com/paper/2407.17161/) (2024)
- [Towards Adapting Federated & Quantum Machine Learning for Network Intrusion Detection: A Survey](https://mlsanity.com/paper/2509.21389/) (2025)
- [An efficient combination strategy for hybird quantum ensemble classifier](https://mlsanity.com/paper/2210.06785/) (2022)
- [Towards Transparency in Coreference Resolution: A Quantum-Inspired Approach](https://mlsanity.com/paper/2312.00688/) (2023)
- [Quantum Ensembling Methods for Healthcare and Life Science](https://mlsanity.com/paper/2506.02213/) (2025)
- [A weighted quantum ensemble of homogeneous quantum classifiers](https://mlsanity.com/paper/2506.07810/) (2025)
- [A Quantum Bagging Algorithm with Unsupervised Base Learners for Label Corrupted Datasets](https://mlsanity.com/paper/2509.07040/) (2025)

## Abstract (arXiv)

A powerful way to improve performance in machine learning is to construct an ensemble that combines the predictions of multiple models. Ensemble methods are often much more accurate and lower variance than the individual classifiers that make them up but have high requirements in terms of memory and computational time. In fact, a large number of alternative algorithms is usually adopted, each requiring to query all available data. We propose a new quantum algorithm that exploits quantum superposition, entanglement and interference to build an ensemble of classification models. Thanks to the generation of the several quantum trajectories in superposition, we obtain B transformations of the quantum state which encodes the training set in only logleft(Bright) operations. This implies exponential growth of the ensemble size while increasing linearly the depth of the correspondent circuit. Furthermore, when considering the overall cost of the algorithm, we show that the training of a single weak classifier impacts additively the overall time complexity rather than multiplicatively, as it usually happens in classical ensemble methods. We also present small-scale experiments on real-world datasets, defining a quantum version of the cosine classifier and using the IBM qiskit environment to show how the algorithms work.