The tricritical point at the learning transition of deformed toric codes is a higher Nishimori critical point where the Edwards-Anderson correlation exponent exactly matches the clean Ising spin exponent and c_eff is greater than 1/2, decreasing under RG flow.
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Parametrized isometric tensor networks called skeletons deform abelian string-net fixed points via symmetry conservation and isometry constraints, connecting topological phases through critical points and enabling efficient classical computation of generalized Pauli string expectations.
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Higher Nishimori Criticality and Exact Results at the Learning Transition of Deformed Toric Codes
The tricritical point at the learning transition of deformed toric codes is a higher Nishimori critical point where the Edwards-Anderson correlation exponent exactly matches the clean Ising spin exponent and c_eff is greater than 1/2, decreasing under RG flow.
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Skeleton of isometric Tensor Network States for Abelian String-Net Models
Parametrized isometric tensor networks called skeletons deform abelian string-net fixed points via symmetry conservation and isometry constraints, connecting topological phases through critical points and enabling efficient classical computation of generalized Pauli string expectations.