{"id":20372,"date":"2024-04-08T15:09:11","date_gmt":"2024-04-08T15:09:11","guid":{"rendered":"https:\/\/platform.qureca.com\/?post_type=tribe_events&p=18801"},"modified":"2024-04-08T16:06:59","modified_gmt":"2024-04-08T16:06:59","slug":"computing-with-physical-systems","status":"publish","type":"tribe_events","link":"https:\/\/www.qureca.com\/fr\/calendar\/computing-with-physical-systems\/","title":{"rendered":"Computing with Physical Systems"},"content":{"rendered":"

There has been an explosion of interest in\u00a0unconventional approaches to computing<\/b>\u00a0with physical systems\u00a0(some recent reviews\/works: [MMB2022<\/a>], [SM2023<\/a>], [WOG+2020<\/a>], [HAK+23<\/a>]). 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\u00a0rethink how we harness physical processes for computing<\/b>\u00a0\u2013 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 \u2013 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 Aspen Winter Conference will bring together both theorists and experimentalists across a broad range of disciplines \u2013 including soft condensed matter, biological physics, neuroscience, machine learning, hard condensed matter, optics, fluid dynamics, and quantum information science \u2013 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:<\/p>\n