Speaker
Description
Adiabatic quantum computation is one approach to solving combinatorial optimization problems. In this talk, we propose a method that first transforms a combinatorial optimization problem into an equivalent continuous optimization problem via polyhedral domain decomposition and then performs adiabatic quantum computation. Since this formulation makes it easy to introduce quantum fluctuations tailored to the problem at hand, it is expected to search for optimal solutions more efficiently than conventional adiabatic quantum computation. We present a physical implementation based on a photonic quantum system and demonstrate through numerical simulations that optimal solutions are obtained with high probability for small but hard problem instances.