PROFILE:   

Dirk Englund received his BS in Physics from Caltech (2002), MS in Electrical Engineering at Stanford, and PhD in Applied Physics also at Stanford (2008). After a postdoctoral fellowship at Harvard University, he joined Columbia University as Assistant Professor of E.E. and of Applied Physics. He joined the MIT EECS faculty in 2013. Major recognitions include the Presidential Early Career Award in Science and Engineering, the Sloan Fellowship in Physics, the OSA's Adolph Lomb Medal, the Bose Research Fellowship, and the A.v. Humboldt Research Fellowship. He is a fellow of the Optica Society.

ABSTRACT:

After several decades of intensive theoretical and experimental efforts, the field of quantum information processing is at a critical moment: special-purpose quantum information processors are at or past the “quantum complexity frontier” where classical computers can no longer predict their outputs: we can “program complexity”, unable to predict the outcome. Meanwhile, new technologies to connect quantum processors by photons give rise to quantum networks with functions impossible on today’s “classical-physics” internet. 

But to harness the power of quantum complexity in “noisy intermediate-scale” quantum computers and networks, we need new methods to control and understand them -- and perhaps to manage noise sufficiently to reach fault tolerance. This talk discusses one approach: large-scale programmable photonic integrated circuits (PICs) designed to control photons and atomic or atom-like quantum memories. 

The second part of the talk considers another “complexity frontier” requiring large-scale control: that encountered in machine learning and signal processing. These problems present new opportunities at the intersection with quantum information technologies -- specifically, we will consider new directions for processing classical and quantum information in deep learning neural networks architectures, with a particular focus on hardware error correction.