The familiar phases of matter we encounter every day—solids, liquids and gases—are well described by the laws of classical physics. Yet when matter is cooled down to very low temperatures, quantum mechanical effects can become important and transform ordinary matter into a quantum phase of matter. Superfluids and superconductors are just two examples of a wide array of quantum phases of matter that display quantum mechanical phenomena which can be harnessed in the design of new quantum technologies.
To discover a new phase of matter, you need to “know what it looks like”—that is, you need to identify a physical signature of the phase matter in experimental or simulation data. This challenge is quite similar to conventional image recognition, a task that machine learning has proven to be very useful for. Herdman will discuss how machine learning methods can be used to discover new exotic quantum phases of matter that otherwise evade conventional attempts at detection.