Machine learning reveals features of spinon Fermi surface
This work addresses the bottleneck of phase characterization in quantum simulations for researchers in condensed matter physics, though it is incremental as it builds on existing methods.
The authors tackled the challenge of characterizing unknown quantum phases by applying a Quantum-Classical hybrid approach (QuCl) to the Kitaev-Heisenberg model, identifying a signature of an intermediate gapless phase interpreted as Friedel oscillations of gapless spinons forming a Fermi surface.
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.