Learning symmetry-protected topological order from trapped-ion experiments
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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_ | 1866913117552771072 |
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| author | Sadoune, Nicolas Pogorelov, Ivan Edmunds, Claire L. Giudici, Giuliano Giudice, Giacomo Marciniak, Christian D. Ringbauer, Martin Monz, Thomas Pollet, Lode |
| author_facet | Sadoune, Nicolas Pogorelov, Ivan Edmunds, Claire L. Giudici, Giuliano Giudice, Giacomo Marciniak, Christian D. Ringbauer, Martin Monz, Thomas Pollet, Lode |
| contents | Classical machine learning has proven remarkably useful in post-processing quantum data, yet typical learning algorithms often require prior training to be effective. In this work, we employ a tensorial kernel support vector machine (TK-SVM) to analyze experimental data produced by trapped-ion quantum computers. This unsupervised method benefits from directly interpretable training parameters, allowing it to identify the non-trivial string-order characterizing symmetry-protected topological (SPT) phases. We apply our technique to two examples: a spin-1/2 model and a spin-1 model, featuring the cluster state and the AKLT state as paradigmatic instances of SPT order, respectively. Using matrix product states, we generate a family of quantum circuits that host a trivial phase and an SPT phase, with a sharp phase transition between them. For the spin-1 case, we implement these circuits on two distinct trapped-ion machines based on qubits and qutrits. Our results demonstrate that the TK-SVM method successfully distinguishes the two phases across all noisy experimental datasets, highlighting its robustness and effectiveness in quantum data interpretation. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_05017 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Learning symmetry-protected topological order from trapped-ion experiments Sadoune, Nicolas Pogorelov, Ivan Edmunds, Claire L. Giudici, Giuliano Giudice, Giacomo Marciniak, Christian D. Ringbauer, Martin Monz, Thomas Pollet, Lode Quantum Physics Strongly Correlated Electrons Classical machine learning has proven remarkably useful in post-processing quantum data, yet typical learning algorithms often require prior training to be effective. In this work, we employ a tensorial kernel support vector machine (TK-SVM) to analyze experimental data produced by trapped-ion quantum computers. This unsupervised method benefits from directly interpretable training parameters, allowing it to identify the non-trivial string-order characterizing symmetry-protected topological (SPT) phases. We apply our technique to two examples: a spin-1/2 model and a spin-1 model, featuring the cluster state and the AKLT state as paradigmatic instances of SPT order, respectively. Using matrix product states, we generate a family of quantum circuits that host a trivial phase and an SPT phase, with a sharp phase transition between them. For the spin-1 case, we implement these circuits on two distinct trapped-ion machines based on qubits and qutrits. Our results demonstrate that the TK-SVM method successfully distinguishes the two phases across all noisy experimental datasets, highlighting its robustness and effectiveness in quantum data interpretation. |
| title | Learning symmetry-protected topological order from trapped-ion experiments |
| topic | Quantum Physics Strongly Correlated Electrons |
| url | https://arxiv.org/abs/2408.05017 |