Genetic optimization of quantum annealing

TL;DR

This paper introduces a genetic algorithm-based numerical method to optimize quantum annealing schedules and control operators, significantly enhancing performance and fidelity in shorter times by avoiding non-adiabatic transitions.

Contribution

It presents a novel genetic algorithm approach to optimize annealing schedules and control operators, improving quantum annealing efficiency and fidelity.

Findings

Optimized annealing schedules outperform standard methods.

Shorter annealing times achieve higher fidelity.

Even simple local controls substantially improve results.

Abstract

The study of optimal control of quantum annealing by modulating the pace of evolution and by introducing a counterdiabatic potential has gained significant attention in recent times. In this work, we present a numerical approach based on genetic algorithms to improve the performance of quantum annealing, which evades the Landau-Zener transitions to navigate to the ground state of the final Hamiltonian with high probability. We optimize the annealing schedules starting from polynomial ansatz by treating their coefficients as chromosomes of the genetic algorithm. We also explore shortcuts to adiabaticity by computing a practically feasible $k$-local optimal driving operator, showing that even for $k=1$ we achieve substantial improvement of the fidelity over the standard annealing solution. With these genetically optimized annealing schedules and/or optimal driving operators, we are able to perform quantum annealing in relatively short time-scales and with larger fidelity compared to traditional approaches.