# Quantum Markovianity as a supervised learning task

## TL;DR

This paper explores using supervised learning, specifically Random Forest Regressor, to estimate the dimension of non-Markovian quantum environments from simulated data, opening new avenues in quantum environment characterization.

## Contribution

It introduces a novel approach applying supervised learning to quantify quantum environment properties, specifically non-Markovianity, using classical simulation data.

## Key findings

- Random Forest effectively estimates quantum environment dimension
- Supervised learning shows promise for quantum environment analysis
- Method could be extended to real quantum systems

## Abstract

Supervised learning algorithms take as input a set of labelled examples and return as output a predictive model. Such models are used to estimate labels for future, previously unseen examples drawn from the same generating distribution. In this paper we investigate the possibility of using supervised learning to estimate the dimension of a non-Markovian quantum environment. Our approach uses an ensemble learning method, the Random Forest Regressor, applied to classically simulated data sets. Our results indicate this is a promising line of research.