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Date:

Download-files:

Time:

Thursday, 18. Dec. 2025

Video-Recording for any system with MP4-support

- Video.mp4 (ca. 451 Mb)

15:15 – 16:25

"Glasses, Chaos, and Neural Networks: A Unified Physical Perspective"

Speaker: Prof. Victor Galitski

(University of Maryland)

Abstract:

This talk will review our recent work on classical and quantum glasses – ubiquitous

systems where strong frustrated interactions prevent them from settling into a

simple state. I will begin with spin glasses from the perspective of chaos theory and

introduce the mean-field formalism of Thouless, Anderson, and Palmer (TAP) to

"visualize" the rugged landscape of glassy metastable minima. The central theme

of the talk is a one-to-one correspondence between classical spin models and neural

networks (NNs), which allows us to transplant spin-glass theory directly into the

study of learning. In this mapping, training a NN corresponds to a family of spin

Hamiltonians parameterized by training time and physically implies the destruction

of the spin glass and the emergence of hidden order associated with the classification

task. This provides an appealing, universal physical picture of why certain neural

networks work, as well as a natural scheme for their quantization. I will introduce a

broad class of quantum neural networks and show their successful experimental

realization on current quantum hardware, including IBM transmon systems and two

types of trapped-ion quantum computers.