Coauthors: Artur Garcia, Miguel Navascues, Antonio Acin

A standard problem in optimization theory is to find the minimum of a polynomial function subject to polynomial inequality constraints. We introduce a generalization of this problem where the optimization variables are not real numbers, but non-commutative variables, i.e., operators acting on Hilbert spaces of arbitrary dimension. We show how semidefinite programming (SDP) can be used to solve this problem. Specifically, we introduce a sequence of SDP relaxations of the original problem, whose optima converge monotically to the global optimum.

Our method can find applications to compute the ground state energy of quantum many-body systems. In particular, it gives a new interpretation to and should strengthens the RDM method used in quantum chemistry to compute electronic energies. Our method provides a computation technique for many-body systems that is not based on states (and thus directly linked to entanglement) but that is rather based on the algebraic structure of quantum operators.