MemComputing: when memory becomes a computing tool

MemComputing is a new physics-based approach to computation that employs time non-locality (memory) to both process and store information on the same physical location [1]. Time non-locality is a remarkable feature since it can generate dynamical long-range order in the system even if its individual units are interacting locally. Long-range order means that the correlations between the machine units do not decay exponentially, rather algebraically, both temporally and spatially. A memcomputing machine then navigates its phase space by following specific trajectories (instantons) which showcase this long-range order, namely during dynamics the machine can change the values of just a few or as many variables in the problem specification as needed to reach the solution efficiently. I will discuss the physics behind MemComputing, its topological and geometrical aspects, and show many examples of its applicability to various combinatorial optimization problems, Machine Learning, and Quantum Mechanics, demonstrating its advantages over traditional approaches and even quantum computing. Work supported by DARPA, DOE, NSF, CMRR, and MemComputing, Inc. (http://memcpu.com/).

[1] M. Di Ventra, MemComputing: Fundamentals and Applications (Oxford University Press, 2022).

About the speaker

Massimiliano Di Ventra obtained his undergraduate degree in Physics summa cum laude from the University of Trieste (Italy) in 1991 and did his PhD studies at the Swiss Federal Institute of Technology in Lausanne in 1993-1997. He is now professor of Physics at the University of California, San Diego. Di Ventra's research interests are in condensed-matter theory and unconventional computing. He has been invited to deliver more than 350 talks worldwide on these topics. He has published more than 300 papers in refereed journals, 5 textbooks, and has 11 granted patents (7 foreign). He is a fellow of the American Association for the Advancement of Science, the American Physical Society, the Institute of Physics, the IEEE and a foreign member of Academia Europaea. In 2018 he was named Highly Cited Researcher by Clarivate Analytics, he is the recipient of the 2020 Feynman Prize for theory in Nanotechnology, and is a 2022 IEEE Nanotechnology Council Distinguished Lecturer. He is the co-founder of MemComputing, Inc. (http://memcpu.com/).