Machine learning reveals features of spinon Fermi surface

arXiv:2306.03143v2 Announce Type: replace-cross
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) …

abstract arxiv challenge cond-mat.dis-nn cond-mat.str-el cs.lg features hybrid hybrid approach machine machine learning mining progress quant-ph quantum simulation surface type