Speedup of high-order unconstrained binary optimization using quantum $${{\mathbb{Z}}}_{2}$$ Z 2 lattice gauge theory

Abstract

Abstract An important and difficult problem in optimization is the high-order unconstrained binary optimization, which can represent many optimization problems more efficiently than quadratic unconstrained binary optimization, but how to quickly solve it has remained difficult. Here, we present an approach by mapping the high-order unconstrained binary optimization to quantum $${{\mathbb{Z}}}_{2}$$ Z 2 lattice gauge theory and propose the gauged local quantum annealing, which is the local quantum annealing protected by the gauge symmetry. We present the quantum algorithm and its corresponding quantum-inspired classical algorithm for this problem and achieve algorithmic speedup by using gauge symmetry. By running the quantum-inspired classical algorithm, we demonstrate that the gauged local quantum annealing reduces the computational time by one order of magnitude from that of the local quantum annealing.