  We introduce quantum fluctuations into the simulated annealing process of
optimization problems, aiming at faster convergence to the optimal state.
Quantum fluctuations cause transitions between states and thus play the same
role as thermal fluctuations in the conventional approach. The idea is tested
by the transverse Ising model, in which the transverse field is a function of
time similar to the temperature in the conventional method. The goal is to find
the ground state of the diagonal part of the Hamiltonian with high accuracy as
quickly as possible. We have solved the time-dependent Schr\"odinger equation
numerically for small size systems with various exchange interactions.
Comparison with the results of the corresponding classical (thermal) method
reveals that the quantum annealing leads to the ground state with much larger
probability in almost all cases if we use the same annealing schedule.
