Noise Classification in Three-Level Quantum Networks by Machine Learning
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arXiv
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866917853222928384 |
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| author | Mukherjee, Shreyasi Penna, Dario Cirinnà, Fabio Paternostro, Mauro Paladino, Elisabetta Falci, Giuseppe Giannelli, Luigi |
| author_facet | Mukherjee, Shreyasi Penna, Dario Cirinnà, Fabio Paternostro, Mauro Paladino, Elisabetta Falci, Giuseppe Giannelli, Luigi |
| contents | 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 | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_01987 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Noise Classification in Three-Level Quantum Networks by Machine Learning Mukherjee, Shreyasi Penna, Dario Cirinnà, Fabio Paternostro, Mauro Paladino, Elisabetta Falci, Giuseppe Giannelli, Luigi Quantum Physics 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. |
| title | Noise Classification in Three-Level Quantum Networks by Machine Learning |
| topic | Quantum Physics |
| url | https://arxiv.org/abs/2405.01987 |