Quantum algorithms for topological and geometric analysis of data
Persistent homology allows identification of topological features in data sets, allowing the efficient extraction of useful information. Here, the authors propose a quantum machine learning algorithm that provides an exponential speed up over known algorithms for topological data analysis.
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| Auteurs principaux: | , , |
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| Format: | article |
| Langue: | EN |
| Publié: |
Nature Portfolio
2016
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| Sujets: | |
| Accès en ligne: | https://doaj.org/article/70f1e67b437a4e34a6db16ebd4c0eb3d |
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| Résumé: | Persistent homology allows identification of topological features in data sets, allowing the efficient extraction of useful information. Here, the authors propose a quantum machine learning algorithm that provides an exponential speed up over known algorithms for topological data analysis. |
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