Papers
Topics
Authors
Recent
Detailed Answer
Quick Answer
Concise responses based on abstracts only
Detailed Answer
Well-researched responses based on abstracts and relevant paper content.
Custom Instructions Pro
Preferences or requirements that you'd like Emergent Mind to consider when generating responses
Gemini 2.5 Flash
Gemini 2.5 Flash 88 tok/s
Gemini 2.5 Pro 52 tok/s Pro
GPT-5 Medium 12 tok/s Pro
GPT-5 High 19 tok/s Pro
GPT-4o 110 tok/s Pro
GPT OSS 120B 470 tok/s Pro
Kimi K2 197 tok/s Pro
2000 character limit reached

Eigenstate Entanglement Entropy in Random Quadratic Hamiltonians (2006.11302v2)

Published 19 Jun 2020 in cond-mat.stat-mech, cond-mat.quant-gas, cond-mat.str-el, hep-th, and quant-ph

Abstract: The eigenstate entanglement entropy has been recently shown to be a powerful tool to distinguish integrable from generic quantum-chaotic models. In integrable models, a unique feature of the average eigenstate entanglement entropy (over all Hamiltonian eigenstates) is that the volume-law coefficient depends on the subsystem fraction. Hence, it deviates from the maximal (subsystem fraction independent) value encountered in quantum-chaotic models. Using random matrix theory for quadratic Hamiltonians, we obtain a closed-form expression for the average eigenstate entanglement entropy as a function of the subsystem fraction. We test its correctness against numerical results for the quadratic Sachdev-Ye-Kitaev model. We also show that it describes the average entanglement entropy of eigenstates of the power-law random banded matrix model (in the delocalized regime), and that it is close but not the same as the result for quadratic models that exhibit localization in quasimomentum space.

List To Do Tasks Checklist Streamline Icon: https://streamlinehq.com

Collections

Sign up for free to add this paper to one or more collections.

Summary

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

Ai Generate Text Spark Streamline Icon: https://streamlinehq.com

Paper Prompts

Sign up for free to create and run prompts on this paper using GPT-5.

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

Follow-up Questions

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