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Fast machine-learning online optimization of ultra-cold-atom experiments
Open accessAbstract
We apply an online optimization process based on machine learning to the production of Bose-Einstein condensates (BEC). BEC is typically created with an exponential evaporation ramp that is optimal for ergodic dynamics with two-body s-wave interactions and no other loss rates, but likely sub-optimal for real experiments. Through repeated machine-controlled scientific experimentation and observations our ‘learner’ discovers an optimal evaporation ramp for BEC production. In contrast to previous work, our learner uses a Gaussian process to develop a statistical model of the relationship between the parameters it controls and the quality of the BEC produced. We demonstrate that the Gaussian process machine learner is able to discover a ramp that produces high quality BECs in 10 times fewer iterations than a previously used online optimization technique. Furthermore, we show the internal model developed can be used to determine which parameters are essential in BEC creation and which are unimportant, providing insight into the optimization process of the system.
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Cites
A Simplex Method for Function Minimization
Nelder, J. A., & Mead, R. (1965) · The Computer Journal · doi:10.1093/comjnl/7.4.308 · Cited by 28,905
Quantum simulations with ultracold quantum gases
Bloch, I., Dalibard, J., & Nascimbène, S. (2012) · Nature Physics · doi:10.1038/nphys2259 · Cited by 2,339
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Metadata
- Authors
- Wigley, P. B., Everitt, P. J., van den Hengel, A., Bastian, J. W., Sooriyabandara, M. A., McDonald, G. D., Hardman, K. S., Quinlivan, C. D., Manju, P., Kuhn, C. C. N., Petersen, I. R., Luiten, A. N., Hope, J. J., Robins, N. P., & Hush, M. R.
- Venue
- Scientific Reports
- Published
- 2016-05-16
- Volume
- 6
- Issue
- 1
- Pages
- 25890-25890
- ISSN
- 2045-2322 (ISSN-L)
- OpenAlex
- W2226799981
- Cited by
- 208
- References
- 2
- FWCI
- 8.73
- Type
- journal article
- Language
- en
- Open access
- gold
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- Wigley, P. B., Everitt, P. J., van den Hengel, A., Bastian, J. W., Sooriyabandara, M. A., McDonald, G. D., Hardman, K. S., Quinlivan, C. D., Manju, P., Kuhn, C. C. N., Petersen, I. R., Luiten, A. N., Hope, J. J., Robins, N. P., & Hush, M. R. (2016). Fast machine-learning online optimization of ultra-cold-atom experiments. Scientific Reports. https://doi.org/10.1038/srep25890
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