Predicting the Onset of Quantum Synchronization Using Machine Learning
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arXiv
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| Hauptverfasser: | , , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2023
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| _version_ | 1866913376551043072 |
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| author | Mahlow, Felipe Çakmak, Barış Karpat, Göktuğ Yalçınkaya, İskender Fanchini, Felipe |
| author_facet | Mahlow, Felipe Çakmak, Barış Karpat, Göktuğ Yalçınkaya, İskender Fanchini, Felipe |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2308_15330 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Predicting the Onset of Quantum Synchronization Using Machine Learning Mahlow, Felipe Çakmak, Barış Karpat, Göktuğ Yalçınkaya, İskender Fanchini, Felipe Quantum Physics 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. |
| title | Predicting the Onset of Quantum Synchronization Using Machine Learning |
| topic | Quantum Physics |
| url | https://arxiv.org/abs/2308.15330 |