  Designing proper time-dependent control fields for slowly varying the system
to the ground state that encodes the problem solution is crucial for adiabatic
quantum computation. However, inevitable perturbations in real applications
demand us to accelerate the evolution so that the adiabatic errors can be
prevented from accumulation. Here, by treating this trade-off task as a
multiobjective optimization problem, we propose a gradient-free learning
algorithm with pulse smoothing technique to search optimal adiabatic quantum
pathways and apply it to the Landau-Zener Hamiltonian and Grover search
Hamiltonian. Numerical comparisons with a linear schedule, local adiabatic
theorem induced schedule, and gradient-based algorithm searched schedule reveal
that the proposed method can achieve significant performance improvements in
terms of the adiabatic time and the instantaneous ground-state population
maintenance. The proposed method can be used to solve more complex and real
adiabatic quantum computation problems.
