Machine learning techniques in modern quantum-mechanics experiments
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. In this talk, we discuss how recent experiments have exploited machine-learning techniques, both to optimize the operation of these devices and to interperet the data they produce.