Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
96 tokens/sec
Gemini 2.5 Pro Premium
48 tokens/sec
GPT-5 Medium
15 tokens/sec
GPT-5 High Premium
23 tokens/sec
GPT-4o
104 tokens/sec
DeepSeek R1 via Azure Premium
77 tokens/sec
GPT OSS 120B via Groq Premium
466 tokens/sec
Kimi K2 via Groq Premium
201 tokens/sec
2000 character limit reached

Distance between two manifolds, topological phase transitions and scaling laws (2405.03323v1)

Published 6 May 2024 in cond-mat.mes-hall

Abstract: Topological phases are generally characterized by topological invariants denoted by integer numbers. However, different topological systems often require different topological invariants to measure, such as geometric phases, topological orders, winding numbers, etc. Moreover, geometric phases and its associated definitions usually fail at critical points. Therefore, it's challenging to predict what would occur during the transformation between two different topological phases. To address these issues, in this work, we propose a general definition based on fidelity and trace distance from quantum information theory: manifold distance. This definition does not rely on the berry connection of the manifolds but rather on the information of the two manifolds - their ground state wave functions. Thus, it can measure different topological systems (including traditional band topology models, non-Hermitian systems, and topological order models, etc.) and exhibit some universal laws during the transformation between two topological phases. Our research demonstrates that when the properties of two manifolds are identical, the distance and associated higher-order derivatives between them can smoothly transition to each other. However, for two different topological manifolds, the higher-order derivatives exhibit various divergent behaviors near the critical points. For subsequent studies, we expect the method to be generalized to real-space or non-lattice models, in order to facilitate the study of a wider range of physical platforms such as open systems and many-body localization.

Summary

We haven't generated a summary for this paper yet.

Dice Question Streamline Icon: https://streamlinehq.com

Follow-up Questions

We haven't generated follow-up questions for this paper yet.

X Twitter Logo Streamline Icon: https://streamlinehq.com