Machine learning reveals features of spinon Fermi surface

Autor: Kevin Zhang, Shi Feng, Yuri D. Lensky, Nandini Trivedi, Eun-Ah Kim
Jazyk: angličtina
Rok vydání: 2024
Předmět:
Zdroj: Communications Physics, Vol 7, Iss 1, Pp 1-8 (2024)
Druh dokumentu: article
ISSN: 2399-3650
23645709
DOI: 10.1038/s42005-024-01542-8
Popis: Abstract With rapid progress in simulation of strongly interacting quantum Hamiltonians, the challenge in characterizing unknown phases becomes a bottleneck for scientific progress. We demonstrate that a Quantum-Classical hybrid approach (QuCl) of mining sampled projective snapshots with interpretable classical machine learning can unveil signatures of seemingly featureless quantum states. The Kitaev-Heisenberg model on a honeycomb lattice under external magnetic field presents an ideal system to test QuCl, where simulations have found an intermediate gapless phase (IGP) sandwiched between known phases, launching a debate over its elusive nature. We use the correlator convolutional neural network, trained on labeled projective snapshots, in conjunction with regularization path analysis to identify signatures of phases. We show that QuCl reproduces known features of established phases. Significantly, we also identify a signature of the IGP in the spin channel perpendicular to the field direction, which we interpret as a signature of Friedel oscillations of gapless spinons forming a Fermi surface. Our predictions can guide future experimental searches for spin liquids.
Databáze: Directory of Open Access Journals
Externí odkaz:
Nepřihlášeným uživatelům se plný text nezobrazuje