Predicting the onset of quantum synchronization using machine learning

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21340%2F24%3A00380372" target="_blank" >RIV/68407700:21340/24:00380372 - isvavai.cz</a>
Result on the web
<a href="https://doi.org/10.1103/PhysRevA.109.052411" target="_blank" >https://doi.org/10.1103/PhysRevA.109.052411</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1103/PhysRevA.109.052411" target="_blank" >10.1103/PhysRevA.109.052411</a>

Result language
angličtina
Original language name
Predicting the onset of quantum synchronization using machine learning
Original language description
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.
Czech name
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Czech description
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Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
10301 - Atomic, molecular and chemical physics (physics of atoms and molecules including collision, interaction with radiation, magnetic resonances, Mössbauer effect)

Project
<a href="/en/project/GA23-07169S" target="_blank" >GA23-07169S: Multipartite quantum dynamics on graphs and hypergraphs – theory and applications</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year
2024
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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