  Quantum annealing is a generic solver of the optimization problem that uses
fictitious quantum fluctuation. Its simulation in classical computing is often
performed using the quantum Monte Carlo simulation via the Suzuki--Trotter
decomposition. However, the negative sign problem sometimes emerges in the
simulation of quantum annealing with an elaborate driver Hamiltonian, since it
belongs to a class of non-stoquastic Hamiltonians. In the present study, we
propose an alternative way to avoid the negative sign problem involved in a
particular class of the non-stoquastic Hamiltonians. To check the validity of
the method, we demonstrate our method by applying it to a simple problem that
includes the anti-ferromagnetic XX interaction, which is a typical instance of
the non-stoquastic Hamiltonians.

Quantum Physics;Disordered Systems and Neural Networks;Statistical Mechanics;Machine Learning