  A hierarchy of semidefinite programming (SDP) relaxations approximates the
global optimum of polynomial optimization problems of noncommuting variables.
Generating the relaxation, however, is a computationally demanding task, and
only problems of commuting variables have efficient generators. We develop an
implementation for problems of noncommuting problems that creates the
relaxation to be solved by SDPA -- a high-performance solver that runs in a
distributed environment. We further exploit the inherent sparsity of
optimization problems in quantum physics to reduce the complexity of the
resulting relaxations. Constrained problems with a relaxation of order two may
contain up to a hundred variables. The implementation is available in Python.
The tool helps solve problems such as finding the ground state energy or
testing quantum correlations.
