Interpreting convolutional neural networks' low-dimensional approximation to quantum spin systems
Convolutional neural networks (CNNs) have been employed along with variational Monte Carlo methods for finding the ground state of quantum many-body spin systems with great success. However, it remains uncertain how CNNs, with a model complexity that scales at most linearly with the number of partic...
Saved in:
Main Authors: | Yilong Ju, Shah Saad Alam, Jonathan Minoff, Fabio Anselmi, Han Pu, Ankit Patel |
---|---|
Format: | Article |
Language: | English |
Published: |
American Physical Society
2025-01-01
|
Series: | Physical Review Research |
Online Access: | http://doi.org/10.1103/PhysRevResearch.7.013094 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Quantum Spin Ice in Three-Dimensional Rydberg Atom Arrays
by: Jeet Shah, et al.
Published: (2025-02-01) -
High-Dimensional Quantum Key Distribution by a Spin-Orbit Microlaser
by: Yichi Zhang, et al.
Published: (2025-02-01) -
Classical and quantum spin liquids
by: Capponi, Sylvain
Published: (2025-01-01) -
Topological Excitations in Quantum Spin Systems
by: Ranjan Chaudhury, et al.
Published: (2013-01-01) -
Nonreciprocal Synchronization of Active Quantum Spins
by: Tobias Nadolny, et al.
Published: (2025-01-01)