For PEPS with strong injectivity above a threshold, belief propagation finds fixed points efficiently and cluster-corrected BP approximates observables to 1/poly(N) error in poly(N) time, with local perturbations affecting the fixed point only locally.
Title resolution pending
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
citation-role summary
background 1
citation-polarity summary
fields
quant-ph 2years
2026 2verdicts
UNVERDICTED 2roles
background 1polarities
background 1representative citing papers
Nonlinear cross-entropy benchmark and heavy-output classifier enable sample-efficient distinction between noisy quantum and classical spoofers for shallow-depth all-to-all random circuits.
citing papers explorer
-
Algorithmic Locality via Provable Convergence in Quantum Tensor Networks
For PEPS with strong injectivity above a threshold, belief propagation finds fixed points efficiently and cluster-corrected BP approximates observables to 1/poly(N) error in poly(N) time, with local perturbations affecting the fixed point only locally.
-
Sample-efficient benchmarking of shallow all-to-all random quantum circuits
Nonlinear cross-entropy benchmark and heavy-output classifier enable sample-efficient distinction between noisy quantum and classical spoofers for shallow-depth all-to-all random circuits.