# Multipartite Entanglement Diagnosis

Multipartite-entanglement diagnosis now takes finite-shot Pauli records directly as input instead of starting from full tomography. Earlier learning-based methods tended to return a single label even when the evidence was thin. This protocol combines binomial Pauli estimators, task-informed observable designs, class-conditional conformal prediction and out-of-distribution abstention to output a single label, a prediction set or an abstention; on synthetic benchmarks, phase-aware global probes reached 0.968 balanced accuracy for perturbed-GHZ discrimination at 20 qubits.

Multipartite-entanglement diagnosis now takes finite-shot Pauli records directly as input instead of starting from full tomography. Earlier learning-based methods tended to return a single label even when the evidence was thin. This protocol combines binomial Pauli estimators, task-informed observable designs, class-conditional conformal prediction and out-of-distribution abstention to output a single label, a prediction set or an abstention; on synthetic benchmarks, phase-aware global probes reached 0.968 balanced accuracy for perturbed-GHZ discrimination at 20 qubits.