Quantum Heuristics for Linear Optimization Over Large Separable Operators

Optimizing over separable quantum objects is challenging for two key reasons: determining separability is NPhard, and the dimensionality of the problem grows exponentially with the number of qubits. We address both challenges by introducing a heuristic algorithm that leverages a quantum coprocessor to significantly reduce the problem's dimensionality. We then numerically demonstrate that see-saw-type optimization performs well in lower-dimensional settings. A notable feature of our approach is that it yields feasible solutions, not just bounds on the optimal value, in contrast to many outer-approximationbased methods. We apply our method to the problem of finding separable states with minimal energy for a given Hamiltonian and use this to define an entanglement measure for its ground space. Finally, we demonstrate how our approach can approximate the separable ground energy of Hamiltonians up to 28 qubits.