Multipartite Entanglement in Crossing the Quantum Critical Point
Hao-Yu Sun, Zi-Yong Ge, and Heng Fan

TL;DR
This paper studies how multipartite entanglement evolves during slow quantum quenches crossing a critical point in the quantum Ising and Lipkin-Meshkov-Glick models, revealing scaling behaviors consistent with the Kibble-Zurek mechanism.
Contribution
It demonstrates how quantum Fisher information quantifies multipartite entanglement dynamics and shows the influence of long-range physics in different quantum models during phase transitions.
Findings
Quantum Fisher information density scales as a power law with quench rate.
Scaling conforms to the Kibble-Zurek mechanism with small corrections.
Long-range behaviors affect entanglement scaling in local systems.
Abstract
We investigate the multipartite entanglement for a slow quantum quench crossing a critical point. We consider the quantum Ising model and the Lipkin-Meshkov-Glick model, which are local and full-connected quantum systems, respectively. The multipartite entanglement is quantified by quantum Fisher information with the generator defined as the operator of the ferromagnetic order parameter. The quench dynamics begins with a ground state in a paramagnetic phase, and then the transverse field is driven slowly to cross a quantum critical point, and ends with a zero transverse field. For the quantum Ising model, based on methods of matrix product states, we calculate the quantum Fisher information density of the final state. Numerical results of both linear and nonlinear quenches show that the quantum Fisher information density of the final state scales as a power law of the quench rate, which…
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
TopicsQuantum many-body systems · Opinion Dynamics and Social Influence · Neural Networks and Reservoir Computing
