Computing with Physical Systems

Organizers:

Andrea J. Liu, University of Pennsylvania
*Peter McMahon, Cornell University
Arvind Murugan, University of Chicago
Hakan E. Türeci, Princeton University

There has been an explosion of interest in unconventional approaches to computing with physical systems. This has been driven by multiple factors, including (1) the realization that there is the potential to build vastly more energy-efficient or faster computers if we rethink how we harness physical processes for computing – giving up some of the abstractions computers have relied on for 50+ years in exchange for being able to operate closer to the fundamental limits that physics allows, and (2) the growth of machine learning – which provides both a strong motivator for more efficient machines to be built, as well as a wealth of methods that can be used to reimagine how computers work. This conference will bring together both theorists and experimentalists across a broad range of disciplines – including soft condensed matter, biological physics, neuroscience, machine learning, hard condensed matter, optics, fluid dynamics, and quantum information science – who typically do not have the opportunity to interact but who are all exploring various aspects of computing in different physical systems.

Topics will include:

• Information processing and dynamics in classical and quantum systems, including (but not limited to) electronic, spintronic, optical, mechanical, fluidic, biological, and chemical systems.

• Devices, architectures, and algorithms for constructing physical machines that can learn without electronic processors.

• Fundamental limits to computing: time, energy, precision.

• Integrated sensing, computation, and actuation.

The conference will feature invited talks and discussion sessions. All participants will be invited to present posters.

For more information, please click here.

*organizer responsible for participant diversity