| Title | Machine Learning Outperforms Physicists in Discovering Bose-Einstein Condensate Parameters | |
| Date | Wednesday May 18 2016, @09:52PM | |
| Author | martyb | |
| Topic | ||
| from the CoolSoftware++ dept. | ||
A machine learning optimization process can outperform physicists when it comes to the specific task of finding new ways to create a Bose-Einstein condensate:
Australian physicists have used an online optimization process based on machine learning to produce effective Bose-Einstein condensates (BECs) in a fraction of the time it would normally take the researchers.
A BEC is a state of matter of a dilute gas of atoms trapped in a laser beam and cooled to temperatures just above absolute zero. BECs are extremely sensitive to external disturbances, which makes them ideal for research into quantum phenomena or for making very precise measurements such as tiny changes in the Earth's magnetic field or gravity.
The experiment, developed by physicists from ANU, University of Adelaide and UNSW ADFA, demonstrated that "machine-learning online optimization" can discover optimized condensation methods "with less experiments than a competing optimization method and provide insight into which parameters are important in achieving condensation," the physicists explain in an open-access paper in the Nature group journal Scientific Reports .
Fast machine-learning online optimization of ultra-cold-atom experiments (open, DOI: 10.1038/srep25890)
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