Machine learning techniques in modern quantum-mechanics experiments

published 7 months ago by Oxford University

In this talk, Dr Elliott Bentine shall discuss how recent experiments have exploited machine-learning techniques, both to optimize the operation of these devices and to interperet the data they produce. Modern table-top experiments can engineer physical systems that are deeply into the quantum mechanical regime. These cutting-edge instruments provide new insights into fundamental physics, and a pathway to future devices that will harness the power of quantum mechanics. They typically require complex operations to prepare and control the quantum state, involving time-dependent sequences of magnetic, electric and laser fields. This presents experimental physicists with an overwhelming number of tunable parameters, which may be subject to uncertainty or fluctuations.

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