  An improved method for driving a system into a desired distribution, for
example, the Gibbs-Boltzmann distribution, is proposed, which makes use of an
artificial relaxation process. The standard techniques for achieving the
Gibbs-Boltzmann distribution involve numerical simulations under the detailed
balance condition. In contrast, in the present study we formulate the Langevin
dynamics, for which the corresponding Fokker-Planck operator includes an
asymmetric component violating the detailed balance condition. This leads to
shifts in the eigenvalues and results in the acceleration of the relaxation
toward the steady state. The numerical implementation demonstrates faster
convergence and shorter correlation time, and the technique of biased event
sampling, Nemoto-Sasa theory, further highlights the efficacy of our method.
