Adrian Feiguin

Sponsor: NSF-DMR (Division of Materials Research: Condensed Matter and Materials Theory) Grant

The many-body problem in the age of quantum machine learning

The study of exotic phases of matter of quantum origin is one of the cornerstones of modern condensed matter physics, motivating a quest for materials and models that could exhibit novel unconventional properties that can find application beyond the semiconductor paradigm. However, understanding correlated quantum systems requires dealing with a large configuration space: datasets are comprised of all possible electronic configurations and cannot be stored in the memory of the largest supercomputer. Hence, the many-body problem can be interpreted as an “extreme data science” problem from an information processing perspective. Since the advent of high-temperature superconductivity, progress has been marked by ingenuity to overcome the computational limitations imposed by hardware. A game-changing idea consists of identifying patterns and compressing datasets in a spirit very similar to algorithms to compress images and videos. Since 2018, we have witnessed the emergence of a novel line of research now referred-to as “quantum machine learning” that uses neural networks and machine-learning algorithms to extract insightful information and represent the complex entanglement structure encoded in quantum wave-functions.