Predicting the Onset of Quantum Synchronization Using Machine Learning

TL;DR

This paper demonstrates that machine learning, specifically k-nearest neighbors, can predict the onset of quantum synchronization in open qubit systems early in their dynamics, aiding experimental detection.

Contribution

It introduces a machine learning approach to predict quantum synchronization onset using early-time data across different open system models, showing high accuracy and robustness.

Findings

High-precision early prediction of quantum synchronization

Robustness against measurement errors demonstrated

Applicability across various dissipation regimes

Abstract

We have applied a machine learning algorithm to predict the emergence of environment-induced spontaneous synchronization between two qubits in an open system setting. In particular, we have considered three different models, encompassing global and local dissipation regimes, to describe the open system dynamics of the qubits. We have utilized the $k$-nearest neighbors algorithm to estimate the long time synchronization behavior of the qubits only using the early time expectation values of qubit observables in these three distinct models. Our findings clearly demonstrate the possibility of determining the occurrence of different synchronization phenomena with high precision even at the early stages of the dynamics using a machine learning-based approach. Moreover, we show the robustness of our approach against potential measurement errors in experiments by considering random errors in qubit expectation values. We believe that the presented results can prove to be useful in experimental studies on the determination of quantum synchronization.