Alexander Zlokapa studies classical and quantum computation using the tools of statistical physics. He extends ideas originally developed to explain thermodynamics and instead explains the computational properties of large, random systems. Such systems include deep neural networks and disordered quantum systems. He aims to understand how neural networks learn features from data and to identify problems in physics that quantum computers can solve efficiently but that remain difficult for classical computers.
Alexander’s work on quantum algorithms for learning from classical data was recently selected as a finalist project in the Google XPRIZE Quantum Applications competition; his undergraduate research was featured on the cover of Nature.
He is a PhD candidate at MIT, advised by Isaac Chuang and Aram Harrow. He earned his B.S. in physics from Caltech, where he received the Richard Feynman Prize in Theoretical Physics and served as assistant concertmaster of the Caltech Orchestra.
