  Variational quantum algorithms are promising candidates for delivering
practical quantum advantage on noisy intermediate-scale quantum (NISQ)
hardware. However, optimizing the noisy cost functions associated with these
algorithms is challenging for system sizes relevant to quantum advantage. In
this work, we investigate the effect of noise on optimization by studying a
variational quantum eigensolver (VQE) algorithm calculating the ground state of
a spin chain model, and we observe an abrupt transition induced by noise to the
optimal solutions. We will present numerical simulations, a demonstration using
an IBM quantum processor unit (QPU), and a theoretical analysis indicating the
origin of this transition. Our findings suggest that careful analysis is
crucial to avoid misinterpreting the noise-induced features as genuine
algorithm results.
