  The relaxation of binary spins to analog values has been the subject of much
debate in the field of statistical physics, neural networks, and more recently
quantum computing, notably because the benefits of using an analog state for
finding lower energy spin configurations are usually offset by the negative
impact of the improper mapping of the energy function that results from the
relaxation. We show that it is possible to destabilize trapping sets of analog
states that correspond to local minima of the binary spin Hamiltonian by
extending the phase space to include error signals that correct amplitude
inhomogeneity of the analog spin states and controlling the divergence of their
velocity. Performance of the proposed analog spin system in finding lower
energy states is competitive against state-of-the-art heuristics.
