Noise classification in three-level quantum networks by Machine Learning
We investigate a machine learning based classification of noise acting on a small quantum network with the aim of detecting spatial or multilevel correlations, and the interplay with Markovianity. We control a three-level system by inducing coherent population transfer exploiting different pulse amp...
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| Format: | Article |
| Language: | English |
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IOP Publishing
2024-01-01
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| Series: | Machine Learning: Science and Technology |
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| Online Access: | https://doi.org/10.1088/2632-2153/ad9193 |
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| author | Shreyasi Mukherjee Dario Penna Fabio Cirinnà Mauro Paternostro Elisabetta Paladino Giuseppe Falci Luigi Giannelli |
| author_facet | Shreyasi Mukherjee Dario Penna Fabio Cirinnà Mauro Paternostro Elisabetta Paladino Giuseppe Falci Luigi Giannelli |
| author_sort | Shreyasi Mukherjee |
| collection | DOAJ |
| description | We investigate a machine learning based classification of noise acting on a small quantum network with the aim of detecting spatial or multilevel correlations, and the interplay with Markovianity. We control a three-level system by inducing coherent population transfer exploiting different pulse amplitude combinations as inputs to train a feedforward neural network. We show that supervised learning can classify different types of classical dephasing noise affecting the system. Three non-Markovian (quasi-static correlated, anti-correlated and uncorrelated) and Markovian noises are classified with more than 99% accuracy. On the contrary, correlations of Markovian noise cannot be discriminated with our method. Our approach is robust to statistical measurement errors and retains its effectiveness for physical measurements where only a limited number of samples is available making it very experimental-friendly. Our result paves the way for classifying spatial correlations of noise in quantum architectures. |
| format | Article |
| id | doaj-art-642e8183aa004ef2b71bf09f52f96d16 |
| institution | OA Journals |
| issn | 2632-2153 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IOP Publishing |
| record_format | Article |
| series | Machine Learning: Science and Technology |
| spelling | doaj-art-642e8183aa004ef2b71bf09f52f96d162025-08-20T02:28:27ZengIOP PublishingMachine Learning: Science and Technology2632-21532024-01-015404504910.1088/2632-2153/ad9193Noise classification in three-level quantum networks by Machine LearningShreyasi Mukherjee0Dario Penna1Fabio Cirinnà2Mauro Paternostro3https://orcid.org/0000-0001-8870-9134Elisabetta Paladino4https://orcid.org/0000-0002-9929-3768Giuseppe Falci5https://orcid.org/0000-0001-5842-2677Luigi Giannelli6https://orcid.org/0000-0001-9704-7304Dipartimento di Fisica e Astronomia ‘Ettore Majorana’, Università di Catania , Via S. Sofia 64, 95123 Catania, ItalyLeonardo S.p.A., Cyber & Security Solutions , 95121 Catania, ItalyLeonardo S.p.A., Cyber & Security Solutions , 95121 Catania, ItalyUniversità degli Studi di Palermo , Dipartimento di Fisica e Chimica—Emilio Segrè,via Archirafi 36, I-90123 Palermo, Italy; Centre for Theoretical Atomic, Molecular, and Optical Physics, School of Mathematics and Physics, Queens University , Belfast BT7 1NN, United KingdomDipartimento di Fisica e Astronomia ‘Ettore Majorana’, Università di Catania , Via S. Sofia 64, 95123 Catania, Italy; INFN , Sezione di Catania, 95123 Catania, Italy; CNR-IMM, UoS Università , 95123 Catania, ItalyDipartimento di Fisica e Astronomia ‘Ettore Majorana’, Università di Catania , Via S. Sofia 64, 95123 Catania, Italy; INFN , Sezione di Catania, 95123 Catania, Italy; CNR-IMM, UoS Università , 95123 Catania, ItalyDipartimento di Fisica e Astronomia ‘Ettore Majorana’, Università di Catania , Via S. Sofia 64, 95123 Catania, Italy; INFN , Sezione di Catania, 95123 Catania, ItalyWe investigate a machine learning based classification of noise acting on a small quantum network with the aim of detecting spatial or multilevel correlations, and the interplay with Markovianity. We control a three-level system by inducing coherent population transfer exploiting different pulse amplitude combinations as inputs to train a feedforward neural network. We show that supervised learning can classify different types of classical dephasing noise affecting the system. Three non-Markovian (quasi-static correlated, anti-correlated and uncorrelated) and Markovian noises are classified with more than 99% accuracy. On the contrary, correlations of Markovian noise cannot be discriminated with our method. Our approach is robust to statistical measurement errors and retains its effectiveness for physical measurements where only a limited number of samples is available making it very experimental-friendly. Our result paves the way for classifying spatial correlations of noise in quantum architectures.https://doi.org/10.1088/2632-2153/ad9193machine learning for quantumthree-level systemnoise classification(non-)Markovianitynoise correlationsquantum network |
| spellingShingle | Shreyasi Mukherjee Dario Penna Fabio Cirinnà Mauro Paternostro Elisabetta Paladino Giuseppe Falci Luigi Giannelli Noise classification in three-level quantum networks by Machine Learning Machine Learning: Science and Technology machine learning for quantum three-level system noise classification (non-)Markovianity noise correlations quantum network |
| title | Noise classification in three-level quantum networks by Machine Learning |
| title_full | Noise classification in three-level quantum networks by Machine Learning |
| title_fullStr | Noise classification in three-level quantum networks by Machine Learning |
| title_full_unstemmed | Noise classification in three-level quantum networks by Machine Learning |
| title_short | Noise classification in three-level quantum networks by Machine Learning |
| title_sort | noise classification in three level quantum networks by machine learning |
| topic | machine learning for quantum three-level system noise classification (non-)Markovianity noise correlations quantum network |
| url | https://doi.org/10.1088/2632-2153/ad9193 |
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