Speedup of high-order unconstrained binary optimization using quantum Z2 lattice gauge theory

TL;DR

This paper introduces a novel approach to high-order unconstrained binary optimization by mapping it to quantum Z2 lattice gauge theory and employing gauged local quantum annealing, achieving significant speedups.

Contribution

It presents a new mapping to quantum Z2 lattice gauge theory and a gauged local quantum annealing method, providing both quantum and classical algorithms with improved efficiency.

Findings

Gauged local quantum annealing reduces computational time by an order of magnitude.

The approach achieves algorithmic speedup using gauge symmetry.

Quantum-inspired classical algorithms outperform traditional methods.

Abstract

An important and difficult problem in optimization is the high-order unconstrained binary optimization, which can represent many optimization problems more efficient 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 Z2 lattice gauge theory defined on the dual graph, 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.