Unsupervised machine learning for detecting mutual independence among eigenstate regimes in interacting quasiperiodic chains
Colin Beveridge, Kathleen Hart, Cassio Rodrigo Cristani, Xiao Li, Enrico Barbierato, Yi-Ting Hsu

TL;DR
This paper introduces an unsupervised machine learning method to analyze eigenstate entanglement spectra, revealing the mutual independence and dependencies among different many-body regimes in quasiperiodic chains.
Contribution
It develops a novel unsupervised learning approach to quantify phase independence in eigenstate spectra, applied to complex many-body quantum systems.
Findings
MBL and thermal regimes are mutually independent.
NEE regime depends on MBL and thermal regimes.
ES in NEE shows mixed MBL-like and thermal-like features.
Abstract
Many-body eigenstates that are neither thermal nor many-body-localized (MBL) were numerically found in certain interacting chains with moderate quasiperiodic potentials. The energy regime consisting of these non-ergodic but extended (NEE) eigenstates has been extensively studied for being a possible many-body mobility edge between the energy-resolved MBL and thermal phases. Recently, the NEE regime was further proposed to be a prethermal phenomenon that generally occurs when different operators spread at sizably different timescales. Here, we numerically examine the mutual independence among the NEE, MBL, and thermal regimes in the lens of eigenstate entanglement spectra (ES). Given the complexity and rich information embedded in ES, we develop an unsupervised learning approach that is designed to quantify the mutual independence among general phases. Our method is first demonstrated on…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsMachine Learning in Materials Science · Advanced Mathematical Modeling in Engineering · Surface Chemistry and Catalysis
