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Entanglement Theory Quantum Correlations
Entanglement phase transitions in random stabilizer tensor networks
arXiv
Authors: Zhi-Cheng Yang, Yaodong Li, Matthew P. A. Fisher, Xiao Chen
Year
2021
Paper ID
62893
Status
Preprint
Abstract Read
~2 min
Abstract Words
262
Citations
N/A
Abstract
We explore a class of random tensor network models with "stabilizer" local tensors which we name Random Stabilizer Tensor Networks (RSTNs). For RSTNs defined on a two-dimensional square lattice, we perform extensive numerical studies of entanglement phase transitions between volume-law and area-law entangled phases of the one-dimensional boundary states. These transitions occur when either (a) the bond dimension D of the constituent tensors is varied, or (b) the tensor network is subject to random breaking of bulk bonds, implemented by forced measurements. In the absence of broken bonds, we find that the RSTN supports a volume-law entangled boundary state with bond dimension Dgeq3 where D is a prime number, and an area-law entangled boundary state for D=2. Upon breaking bonds at random in the bulk with probability p, there exists a critical measurement rate pc for each Dgeq 3 above which the boundary state becomes area-law entangled. To explore the conformal invariance at these entanglement transitions for different prime D, we consider tensor networks on a finite rectangular geometry with a variety of boundary conditions, and extract universal operator scaling dimensions via extensive numerical calculations of the entanglement entropy, mutual information and mutual negativity at their respective critical points. Our results at large D approach known universal data of percolation conformal field theory, while showing clear discrepancies at smaller D, suggesting a distinct entanglement transition universality class for each prime D. We further study universal entanglement properties in the volume-law phase and demonstrate quantitative agreement with the recently proposed description in terms of a directed polymer in a random environment.
Why This Paper Matters
- This paper contributes to the Entanglement Theory & Quantum Correlations research area in the Quantum Articles archive.
- It adds a 2021 reference point for readers tracking recent quantum research.
- We explore a class of random tensor network models with "stabilizer" local tensors which we name Random Stabilizer Tensor Networks (RSTNs).
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