Speaker
Description
Genuine multipartite entanglement captures collective quantum correlations that cannot be described by convex mixtures of states separable across different bipartitions. Quantifying such correlations in quantum many-body states is essential for characterizing their irreducibly collective quantum structure and assessing their potential as multipartite quantum resources, yet remains computationally demanding: both the dimension of the reduced density matrix (RDM) and the number of bipartitions that must be considered grow exponentially with subsystem size. Here, by exploiting the bipartition-wise structure of the constraints in the underlying semidefinite program (SDP), we develop an efficient parallel optimization framework for evaluating genuine multipartite negativity (GMN) from many-body RDMs. Our approach enables GMN calculations for RDMs of up to 10–11 qubits, compared with a practical limit of around 6 qubits for standard SDP solvers. By extending the accessible subsystem size, our framework enables systematic mapping of GMN over a broader range of cluster sizes and geometries in quantum many-body systems, facilitating comparisons across physical regimes.