A Genetic Quantum Annealing Algorithm

TL;DR

This paper introduces a novel genetic quantum annealing algorithm that combines classical genetic algorithms with quantum annealers, leading to improved performance on optimization problems by leveraging quantum-enhanced mutation and inheritance mechanisms.

Contribution

The paper presents a new hybrid algorithm that integrates quantum annealing into genetic algorithms, introducing quantum-inspired mutation and inheritance strategies for optimization.

Findings

Significantly outperforms classical GAs on simple problems

Inherits fitness-based couplings for directed mutation

Utilizes quantum influence to enhance solution search

Abstract

A genetic algorithm (GA) is a search-based optimization technique based on the principles of Genetics and Natural Selection. We present an algorithm which enhances the classical GA with input from quantum annealers. As in a classical GA, the algorithm works by breeding a population of possible solutions based on their fitness. However, the population of individuals is defined by the continuous couplings on the quantum annealer, which then give rise via quantum annealing to the set of corresponding phenotypes that represent attempted solutions. This introduces a form of directed mutation into the algorithm that can enhance its performance in various ways. Two crucial enhancements come from the continuous couplings having strengths that are inherited from the fitness of the parents (so-called nepotism) and from the annealer couplings allowing the entire population to be influenced by the fittest individuals (so-called quantum-polyandry). We find our algorithm to be significantly more powerful on several simple problems than a classical GA.