Unsupervised Machine Learning for Topological Phase Discovery in Floquet Systems
Analysis
This paper introduces a novel unsupervised machine learning framework for classifying topological phases in periodically driven (Floquet) systems. The key innovation is the use of a kernel defined in momentum-time space, constructed from Floquet-Bloch eigenstates. This data-driven approach avoids the need for prior knowledge of topological invariants and offers a robust method for identifying topological characteristics encoded within the Floquet eigenstates. The work's significance lies in its potential to accelerate the discovery of novel non-equilibrium topological phases, which are difficult to analyze using conventional methods.
Key Takeaways
- •Proposes an unsupervised machine learning framework for classifying topological phases in Floquet systems.
- •Uses a kernel defined in momentum-time space, constructed from Floquet-Bloch eigenstates.
- •Data-driven approach avoids the need for prior knowledge of topological invariants.
- •Demonstrates robust identification of topological invariants across various symmetry classes.
- •Aims to accelerate the discovery of novel non-equilibrium topological phases.
“This work successfully reveals the intrinsic topological characteristics encoded within the Floquet eigenstates themselves.”