  The Path Integral Monte Carlo simulated Quantum Annealing algorithm is
applied to the optimization of a large hard instance of the Random 3-SAT
Problem (N=10000). The dynamical behavior of the quantum and the classical
annealing are compared, showing important qualitative differences in the way of
exploring the complex energy landscape of the combinatorial optimization
problem. At variance with the results obtained for the Ising spin glass and for
the Traveling Salesman Problem, in the present case the linear-schedule Quantum
Annealing performance is definitely worse than Classical Annealing.
Nevertheless, a quantum cooling protocol based on field-cycling and able to
outperform standard classical simulated annealing over short time scales is
introduced.
