# The Quantum Version of Prediction for Binary Classification Problem by Ensemble Methods

arXiv: https://arxiv.org/abs/2112.13346 · HTML: https://mlsanity.com/paper/2112.13346/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 18 references that are in the map.
- First posted on arXiv 26 Dec 2021.

## Builds on (most cited first, 2 of 2)

- [Quantum Computing: Lecture Notes](https://mlsanity.com/paper/1907.09415/) (2019)
- [The Quantum Version Of Classification Decision Tree Constructing Algorithm C5.0](https://mlsanity.com/paper/1907.06840/) (2019)

## Built on by (most cited first, 4 of 4)

- [A kernel-based quantum random forest for improved classification](https://mlsanity.com/paper/2210.02355/) (2022)
- [The Improvement of Decision Tree Construction Algorithm Based On Quantum Heuristic Algorithms](https://mlsanity.com/paper/2212.14725/) (2022)
- [Quantum Circuit for Random Forest Prediction](https://mlsanity.com/paper/2312.16877/) (2023)
- [Quantum Random Forest for the Regression Problem](https://mlsanity.com/paper/2603.22790/) (2026)

## Abstract (arXiv)

In this work, we consider the performance of using a quantum algorithm to predict a result for a binary classification problem if a machine learning model is an ensemble from any simple classifiers. Such an approach is faster than classical prediction and uses quantum and classical computing, but it is based on a probabilistic algorithm. Let N be a number of classifiers from an ensemble model and O(T) be the running time of prediction on one classifier. In classical case, an ensemble model gets answers from each classifier and "averages" the result. The running time in classical case is Oleft( N · T right). We propose an algorithm which works in Oleft(√N · Tright).