Kernel-based quantum regressor models learn non-Markovianity

TL;DR

This paper explores quantum kernel-based models, such as quantum support vector machines and kernel ridge regression, for predicting non-Markovianity in quantum systems, demonstrating their accuracy through digital quantum simulations.

Contribution

It introduces quantum kernel methods for non-Markovianity prediction and compares their performance with classical models, showing comparable accuracy.

Findings

Quantum models accurately predict non-Markovianity.

Quantum kernel methods perform comparably to classical models.

Different kernel functions effectively map quantum data.

Abstract

Quantum machine learning is a growing research field that aims to perform machine learning tasks assisted by a quantum computer. Kernel-based quantum machine learning models are paradigmatic examples where the kernel involves quantum states, and the Gram matrix is calculated from the overlap between these states. With the kernel at hand, a regular machine learning model is used for the learning process. In this paper we investigate the quantum support vector machine and quantum kernel ridge models to predict the degree of non-Markovianity of a quantum system. We perform digital quantum simulation of amplitude damping and phase damping channels to create our quantum dataset. We elaborate on different kernel functions to map the data and kernel circuits to compute the overlap between quantum states. We show that our models deliver accurate predictions that are comparable with the fully classical models.