Meta-learning characteristics and dynamics of quantum systems
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arXiv
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| Auteurs principaux: | , , , , , , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866915196244590592 |
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| author | Schorling, Lucas Vaidhyanathan, Pranav Schuff, Jonas Carballido, Miguel J. Zumbühl, Dominik Milburn, Gerard Marquardt, Florian Foerster, Jakob Osborne, Michael A. Ares, Natalia |
| author_facet | Schorling, Lucas Vaidhyanathan, Pranav Schuff, Jonas Carballido, Miguel J. Zumbühl, Dominik Milburn, Gerard Marquardt, Florian Foerster, Jakob Osborne, Michael A. Ares, Natalia |
| contents | While machine learning holds great promise for quantum technologies, most current methods focus on predicting or controlling a specific quantum system. Meta-learning approaches, however, can adapt to new systems for which little data is available, by leveraging knowledge obtained from previous data associated with similar systems. In this paper, we meta-learn dynamics and characteristics of closed and open two-level systems, as well as the Heisenberg model. Based on experimental data of a Loss-DiVincenzo spin-qubit hosted in a Ge/Si core/shell nanowire for different gate voltage configurations, we predict qubit characteristics i.e. $g$-factor and Rabi frequency using meta-learning. The algorithm we introduce improves upon previous state-of-the-art meta-learning methods for physics-based systems by introducing novel techniques such as adaptive learning rates and a global optimizer for improved robustness and increased computational efficiency. We benchmark our method against other meta-learning methods, a vanilla transformer, and a multilayer perceptron, and demonstrate improved performance. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_10492 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Meta-learning characteristics and dynamics of quantum systems Schorling, Lucas Vaidhyanathan, Pranav Schuff, Jonas Carballido, Miguel J. Zumbühl, Dominik Milburn, Gerard Marquardt, Florian Foerster, Jakob Osborne, Michael A. Ares, Natalia Quantum Physics Mesoscale and Nanoscale Physics Machine Learning Computational Physics While machine learning holds great promise for quantum technologies, most current methods focus on predicting or controlling a specific quantum system. Meta-learning approaches, however, can adapt to new systems for which little data is available, by leveraging knowledge obtained from previous data associated with similar systems. In this paper, we meta-learn dynamics and characteristics of closed and open two-level systems, as well as the Heisenberg model. Based on experimental data of a Loss-DiVincenzo spin-qubit hosted in a Ge/Si core/shell nanowire for different gate voltage configurations, we predict qubit characteristics i.e. $g$-factor and Rabi frequency using meta-learning. The algorithm we introduce improves upon previous state-of-the-art meta-learning methods for physics-based systems by introducing novel techniques such as adaptive learning rates and a global optimizer for improved robustness and increased computational efficiency. We benchmark our method against other meta-learning methods, a vanilla transformer, and a multilayer perceptron, and demonstrate improved performance. |
| title | Meta-learning characteristics and dynamics of quantum systems |
| topic | Quantum Physics Mesoscale and Nanoscale Physics Machine Learning Computational Physics |
| url | https://arxiv.org/abs/2503.10492 |