# Quantum ensembles of quantum classifiers

arXiv: https://arxiv.org/abs/1704.02146 · HTML: https://mlsanity.com/paper/1704.02146/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Apr 2017.

## Built on by (most cited first, 10 of 10)

- [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)
- [A kernel-based quantum random forest for improved classification](https://mlsanity.com/paper/2210.02355/) (2022)
- [From Graphs to Qubits: A Critical Review of Quantum Graph Neural Networks](https://mlsanity.com/paper/2408.06524/) (2024)
- [Machine Learning Research Trends in Africa: A 30 Years Overview with Bibliometric Analysis Review](https://mlsanity.com/paper/2304.07542/) (2023)
- [STIQ: Safeguarding Training and Inferencing of Quantum Neural Networks from Untrusted Cloud](https://mlsanity.com/paper/2405.18746/) (2024)
- [Old Rules in a New Game: Mapping Uncertainty Quantification to Quantum Machine Learning](https://mlsanity.com/paper/2507.14919/) (2025)
- [Quantum Boosting using Domain-Partitioning Hypotheses](https://mlsanity.com/paper/2110.12793/) (2021)
- [Quantum Inspired Adaptive Boosting](https://mlsanity.com/paper/2102.00949/) (2021)
- [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)

Quantum machine learning witnesses an increasing amount of quantum algorithms for data-driven decision making, a problem with potential applications ranging from automated image recognition to medical diagnosis. Many of those algorithms are implementations of quantum classifiers, or models for the classification of data inputs with a quantum computer. Following the success of collective decision making with ensembles in classical machine learning, this paper introduces the concept of quantum ensembles of quantum classifiers. Creating the ensemble corresponds to a state preparation routine, after which the quantum classifiers are evaluated in parallel and their combined decision is accessed by a single-qubit measurement. This framework naturally allows for exponentially large ensembles in which -- similar to Bayesian learning -- the individual classifiers do not have to be trained. As an example, we analyse an exponentially large quantum ensemble in which each classifier is weighed according to its performance in classifying the training data, leading to new results for quantum as well as classical machine learning.