# EntNet

Detecting many-body entanglement no longer requires reconstructing the full density matrix or relying on a predefined witness. EntNet turns two- to four-body Pauli correlations on a one-dimensional spin chain into a representation invariant under local basis rotations, sparsely selects 16 feature channels, and learns their spatial arrangement with a Transformer using rotary positional embeddings. On a finite-temperature XY benchmark it reaches 100% AUROC from expectation values of these low-order observables alone and holds the false-positive rate to 0.003 on 2,000 constructively separable states; the same model transfers without retraining to XXZ, frustrated J1-J2 and random local Hamiltonians and to longer chains.

Detecting many-body entanglement no longer requires reconstructing the full density matrix or relying on a predefined witness. EntNet turns two- to four-body Pauli correlations on a one-dimensional spin chain into a representation invariant under local basis rotations, sparsely selects 16 feature channels, and learns their spatial arrangement with a Transformer using rotary positional embeddings. On a finite-temperature XY benchmark it reaches 100% AUROC from expectation values of these low-order observables alone and holds the false-positive rate to 0.003 on 2,000 constructively separable states; the same model transfers without retraining to XXZ, frustrated J1-J2 and random local Hamiltonians and to longer chains.