Machine Learning-aided Optimal Control of a noisy qubit
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| Main Authors: | , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866913948775743488 |
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| author | Cantone, Riccardo Mukherjee, Shreyasi Giannelli, Luigi Paladino, Elisabetta Falci, Giuseppe |
| author_facet | Cantone, Riccardo Mukherjee, Shreyasi Giannelli, Luigi Paladino, Elisabetta Falci, Giuseppe |
| contents | We apply a graybox machine-learning framework to model and control a qubit undergoing Markovian and non-Markovian dynamics from environmental noise. The approach combines physics-informed equations with a lightweight transformer neural network based on the self-attention mechanism. The model is trained on simulated data and learns an effective operator that predicts observables accurately, even in the presence of memory effects. We benchmark both non-Gaussian random-telegraph noise and Gaussian Ornstein-Uhlenbeck noise and achieve low prediction errors even in challenging noise coupling regimes. Using the model as a dynamics emulator, we perform gradient-based optimal control to identify pulse sequences implementing a universal set of single-qubit gates, achieving fidelities above 99% for the lowest considered value of the coupling and remaining above 90% for the highest. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2507_14085 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Machine Learning-aided Optimal Control of a noisy qubit Cantone, Riccardo Mukherjee, Shreyasi Giannelli, Luigi Paladino, Elisabetta Falci, Giuseppe Quantum Physics Other Condensed Matter We apply a graybox machine-learning framework to model and control a qubit undergoing Markovian and non-Markovian dynamics from environmental noise. The approach combines physics-informed equations with a lightweight transformer neural network based on the self-attention mechanism. The model is trained on simulated data and learns an effective operator that predicts observables accurately, even in the presence of memory effects. We benchmark both non-Gaussian random-telegraph noise and Gaussian Ornstein-Uhlenbeck noise and achieve low prediction errors even in challenging noise coupling regimes. Using the model as a dynamics emulator, we perform gradient-based optimal control to identify pulse sequences implementing a universal set of single-qubit gates, achieving fidelities above 99% for the lowest considered value of the coupling and remaining above 90% for the highest. |
| title | Machine Learning-aided Optimal Control of a noisy qubit |
| topic | Quantum Physics Other Condensed Matter |
| url | https://arxiv.org/abs/2507.14085 |