%A Nishimori,Hidetoshi
%A Takada,Kabuki
%D 2017
%J Frontiers in ICT
%C
%F
%G English
%K Quantum Annealing,Quantum adiabatic algorithm,stoquastic Hamiltonian,non-stoquastic Hamiltonian,exponential speedup
%Q
%R 10.3389/fict.2017.00002
%W
%L
%N 2
%M
%P
%7
%8 2017-February-17
%9 Hypothesis and Theory
%+ Hidetoshi Nishimori,Department of Physics, Tokyo Institute of Technology,Japan,nishimori@phys.titech.ac.jp
%#
%! Exponential Enhancement of the Efficiency of Quantum Annealing by Non-Stoquastic Hamiltonians
%*
%<
%T Exponential Enhancement of the Efficiency of Quantum Annealing by Non-Stoquastic Hamiltonians
%U https://www.frontiersin.org/article/10.3389/fict.2017.00002
%V 4
%0 JOURNAL ARTICLE
%@ 2297-198X
%X Non-stoquastic Hamiltonians have both positive and negative signs in off-diagonal elements in their matrix representation in the standard computational basis and thus cannot be simulated efficiently by the standard quantum Monte Carlo method due to the sign problem. We describe our analytical studies of this type of Hamiltonians with infinite-range non-random as well as random interactions from the perspective of possible enhancement of the efficiency of quantum annealing or adiabatic quantum computing. It is shown that multi-body transverse interactions like XX and XXXXX with positive coefficients appended to a stoquastic transverse-field Ising model render the Hamiltonian non-stoquastic and reduce a first-order quantum phase transition in the simple transverse-field case to a second-order transition. This implies that the efficiency of quantum annealing is exponentially enhanced, because a first-order transition has an exponentially small energy gap (and therefore exponentially long computation time) whereas a second-order transition has a polynomially decaying gap (polynomial computation time). The examples presented here represent rare instances where strong quantum effects, in the sense that they cannot be efficiently simulated in the standard quantum Monte Carlo, have analytically been shown to exponentially enhance the efficiency of quantum annealing for combinatorial optimization problems.