Predicting the Onset of Quantum Synchronization Using Machine Learning

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-neighbor 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 the qubit expectation values, initialization errors, as well as deviations in the environment temperature. We believe that the presented results can prove to be useful in experimental studies on the determination of quantum synchronization. © 2024 American Physical Society.Office of Naval Research, ONR: N62909-24-1-2012; Office of Naval Research, ONR; Türkiye Bilimsel ve Teknolojik Araştırma Kurumu, TÜBİTAK: 121F246; Türkiye Bilimsel ve Teknolojik Araştırma Kurumu, TÜBİTAK; Fundação de Amparo à Pesquisa do Estado de São Paulo, FAPESP: 2023/04987-6; Fundação de Amparo à Pesquisa do Estado de São Paulo, FAPESP; Coordenação de Aperfeiçoamento de Pessoal de Nível Superior, CAPES: 88887.607339/2021-00; Coordenação de Aperfeiçoamento de Pessoal de Nível Superior, CAPES; Grantová Agentura České Republiky, GA ČR: GA CR 23-07169S; Grantová Agentura České Republiky, GA Č