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A hybrid machine learning algorithm for designing quantum experiments

O’Driscoll, L.; Nichols, R.; Knott, P. A.

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Authors

L. O’Driscoll

R. Nichols

P. A. Knott



Abstract

We introduce a hybrid machine-learning algorithm for designing quantum optics experiments to produce specific quantum states. Our algorithm successfully found experimental schemes to produce all 5 states we asked it to, including Schrödinger cat states and cubic phase states, all to a fidelity of over 96%. Here we specifically focus on designing realistic experiments, and hence all of the algorithm’s designs only contain experimental elements that are available with current technology. The core of our algorithm is a genetic algorithm that searches for optimal arrangements of the experimental elements, but to speed up the initial search we incorporate a neural network that classifies quantum states. The latter is of independent interest, as it quickly learned to accurately classify quantum states given their photon-number distributions.

Citation

O’Driscoll, L., Nichols, R., & Knott, P. A. (2019). A hybrid machine learning algorithm for designing quantum experiments. Quantum Machine Intelligence, 1(1-2), 5-15. https://doi.org/10.1007/s42484-019-00003-8

Journal Article Type Article
Acceptance Date Feb 28, 2019
Online Publication Date Mar 27, 2019
Publication Date Mar 27, 2019
Deposit Date Mar 4, 2019
Publicly Available Date Jul 9, 2019
Journal Quantum Machine Intelligence
Print ISSN 2524-4906
Electronic ISSN 2524-4914
Publisher Springer
Peer Reviewed Peer Reviewed
Volume 1
Issue 1-2
Pages 5-15
DOI https://doi.org/10.1007/s42484-019-00003-8
Keywords Machine learning; Genetic algorithm; Artificial intelligence; Quantum state engineering; Quantum optics
Public URL https://nottingham-repository.worktribe.com/output/1603532
Publisher URL https://link.springer.com/article/10.1007/s42484-019-00003-8
Contract Date Mar 4, 2019

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