Journal
INTERNATIONAL JOURNAL OF QUANTUM INFORMATION
Volume 16, Issue 8, Pages -Publisher
WORLD SCIENTIFIC PUBL CO PTE LTD
DOI: 10.1142/S0219749918400105
Keywords
Quantum non-Markovianity; supervised learning; random forest
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Funding
- Australian Research Council Centre of Excellence for Quantum Engineered Systems grant [CE 110001013]
- Australian Research Council Discovery Early Career Researcher Award [DE170100712]
- John Templeton Foundation
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Supervised learning algorithms take as input a set of labeled examples and return as output a predictive model. Such models are used to estimate labels for future, previously unseen examples, drawn from the same generating distribution. In this paper, we investigate the possibility of using supervised learning to estimate the dimension of a non-Markovian quantum environment. Our approach uses an ensemble learning method, the Random Forest Regressor, applied to classically simulated datasets. Our results indicate this is a promising line of research.
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