Tensor Network Representations for Intrinsically Mixed-State Topological Orders
Bader Aldossari, Sergey Blinov, Zhu-Xi Luo

TL;DR
This paper introduces a method to construct tensor network representations for mixed-state topological phases, enabling analysis of complex quantum states with topological order affected by decoherence or disorder.
Contribution
The authors develop a general protocol leveraging anyon condensation in Choi states to represent mixed topological phases, including non-Abelian and chiral phases, which were previously difficult to model.
Findings
Constructed tensor network representations for various decohered topological codes.
Demonstrated applicability to non-Abelian and chiral topological phases.
Provided examples including $ ext{Z}_N$ toric code and $S_3$ quantum double.
Abstract
Tensor networks are an efficient platform to represent interesting quantum states of matter as well as to compute physical observables and information-theoretic quantities. We present a general protocol to construct fixed-point tensor network representations for intrinsically mixed-state topological phases, which exhibit nontrivial topological phenomena and do not have pure-state counterparts. The method exploits the power of anyon condensation in Choi states and is applicable to the cases where the target states arise from pure-state topological phases subject to strong decoherence/disorders in the Abelian sectors. Representative examples include decoherence of toric code, decohered non-Abelian quantum double as well as pure / decoherence of arbitrary CSS codes. An example of chiral topological phases which cannot arise from local commuting…
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Taxonomy
TopicsComputational Physics and Python Applications · Quantum many-body systems · Parallel Computing and Optimization Techniques
