# Quantum Circuit for Random Forest Prediction

arXiv: https://arxiv.org/abs/2312.16877 · HTML: https://mlsanity.com/paper/2312.16877/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 8 references that are in the map.
- First posted on arXiv 28 Dec 2023.

## Builds on (most cited first, 1 of 1)

- [The Quantum Version of Prediction for Binary Classification Problem by Ensemble Methods](https://mlsanity.com/paper/2112.13346/) (2021)

## Built on by (most cited first, 1 of 1)

- [Quantum Random Forest for the Regression Problem](https://mlsanity.com/paper/2603.22790/) (2026)

## Abstract (arXiv)

In this work, we present a quantum circuit for a binary classification prediction algorithm using a random forest model. The quantum prediction algorithm is presented in our previous works. We construct a circuit and implement it using qiskit tools (python module for quantum programming). One of our goals is reducing the number of basic quantum gates (elementary gates). The set of basic quantum gates which we use in this work consists of single-qubit gates and a controlled NOT gate. The number of CNOT gates in our circuit is estimated by O(2n+2h+1) , when trivial circuit decomposition techniques give O(4|X|+n+h+2) CNOT gates, where n is the number of trees in a random forest model, h is a tree height and |X| is the length of attributes of an input object X. The prediction process returns an index of the corresponding class for the input X.