Genetic optimization of quantum annealing

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 the 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 timescales and with higher fidelity compared to traditional approaches.

Automated summary

The study presents a numerical approach based on genetic algorithms to optimize the performance of quantum annealing by avoiding Landau-Zener transitions. The optimized annealing schedules and optimal driving operators achieved higher fidelity and shorter timescales compared to traditional approaches.