Learning to Classify Quantum Phases of Matter with a Few Measurements
Fuente:
arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866916386210578432 |
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| author | Khosrojerdi, Mehran Pereira, Jason L. Cuccoli, Alessandro Banchi, Leonardo |
| author_facet | Khosrojerdi, Mehran Pereira, Jason L. Cuccoli, Alessandro Banchi, Leonardo |
| contents | We study the identification of quantum phases of matter, at zero temperature, when only part of the phase diagram is known in advance. Following a supervised learning approach, we show how to use our previous knowledge to construct an observable capable of classifying the phase even in the unknown region. By using a combination of classical and quantum techniques, such as tensor networks, kernel methods, generalization bounds, quantum algorithms, and shadow estimators, we show that, in some cases, the certification of new ground states can be obtained with a polynomial number of measurements. An important application of our findings is the classification of the phases of matter obtained in quantum simulators, e.g., cold atom experiments, capable of efficiently preparing ground states of complex many-particle systems and applying simple measurements, e.g., single qubit measurements, but unable to perform a universal set of gates. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_05188 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Learning to Classify Quantum Phases of Matter with a Few Measurements Khosrojerdi, Mehran Pereira, Jason L. Cuccoli, Alessandro Banchi, Leonardo Quantum Physics Other Condensed Matter Statistical Mechanics Machine Learning We study the identification of quantum phases of matter, at zero temperature, when only part of the phase diagram is known in advance. Following a supervised learning approach, we show how to use our previous knowledge to construct an observable capable of classifying the phase even in the unknown region. By using a combination of classical and quantum techniques, such as tensor networks, kernel methods, generalization bounds, quantum algorithms, and shadow estimators, we show that, in some cases, the certification of new ground states can be obtained with a polynomial number of measurements. An important application of our findings is the classification of the phases of matter obtained in quantum simulators, e.g., cold atom experiments, capable of efficiently preparing ground states of complex many-particle systems and applying simple measurements, e.g., single qubit measurements, but unable to perform a universal set of gates. |
| title | Learning to Classify Quantum Phases of Matter with a Few Measurements |
| topic | Quantum Physics Other Condensed Matter Statistical Mechanics Machine Learning |
| url | https://arxiv.org/abs/2409.05188 |