Quantum Materials for Energy Efficient Neuromorphic Computing (Q-MEEN-C)

Director

Ivan Schuller

Lead Institution

University of California, San Diego

Class

2018-2026

Mission

To lay down the quantum-materials-based foundation for the development of an energy-efficient, fault-tolerant computer that is inspired and works like a brain (“neuromorphic”).

Partner Institutions

UC San Diego
UC Davis
UC Santa Barbara
NYU
Rutgers - New Brunswick
Univ of Chicago
UIUC
Univ of Tennessee
BNL

Quick Facts

Single Slide Overview
Technical Summary
https://qmeenc.ucsd.edu/

BES Staff Contact

Aaron Holder

Grand Challenges Addressed

Transformative Opportunities Addressed

Mastering Hierarchical Architectures and Beyond-Equilibrium Matter

Trans. Opp. #1

Understanding the Critical Roles of Heterogeneity, Interfaces, and Disorder

Trans. Opp. #2

Revolutionary Advances in Models, Mathematics, Algorithms, Data, and Computing

Trans. Opp. #4

Exploiting Transformative Advances in Imaging Capabilities across Multiple Scales

Trans. Opp. #5

BES Reports Addressed

Highlights

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A metallic line atop a medium biased at the edge of chaos can provide effective negative resistance for time-varying signals, outputting a larger signal compared to its input. The energy for amplification comes from the static bias applied to the medium.

The “Edge of Chaos” Amplifies Signals Without Transistors

Emulating the edge of chaos of axons enables a metal wire to overcome its resistance without cooling, thereby amplifying signals flowing inside of it.

Infrared laser light (red beam) illuminates the surface of ZrSiSe and mixes with electron oscillations enabled by an atomic force microscope (silver cone), propagating light as ray-like structures that guide the light through the interior (blue zig-zag).

What If Metals Could Conduct Light?

In the unusual world of quantum materials, metals can guide light in their interiors instead of merely reflecting it.