  This paper introduces a structure-inspired ansatz for addressing quadratic
unconstrained binary optimization problems with the Variational Quantum
Eigensolver. We propose a novel warm start technique that is based on imaginary
time evolution, and allows for determining a set of initial parameters
prioritizing lower energy states in a resource-efficient way. Using classical
simulations, we demonstrate that this warm start method significantly improves
the success rate and reduces the number of iterations required for the
convergence of Variational Quantum Eigensolver. The numerical results also
indicate that the warm start approach effectively mitigates statistical errors
arising from a finite number of measurements, and to a certain extent
alleviates the effect of barren plateaus.
