Steered quantum annealing: improving time efficiency with partial information

TL;DR

This paper introduces steered quantum annealing, a method that uses partial problem information to enlarge the energy gap during annealing, thereby improving efficiency and robustness in quantum optimization.

Contribution

It proposes a novel steered quantum annealing approach that starts from a biased Hamiltonian to increase the energy gap, based on assumptions about the problem instance.

Findings

Larger average energy gap observed in simulations.

Enhanced robustness of quantum annealing process.

Potential for more efficient quantum optimization.

Abstract

In the computational model of quantum annealing, the size of the minimum gap between the ground state and the first excited state of the system is of particular importance, since it is inversely proportional to the running time of the algorithm. Thus, it is desirable to keep the gap as large as possible during the annealing process, since it allows the computation to remain under the protection of the adiabatic theorem while staying efficient. We propose steered quantum annealing as a new method to enlarge the gap throughout the process, in the case of diagonal final Hamiltonians, based on the exploitation of some assumptions we can make about the particular problem instance. In order to introduce this information, we propose beginning the anneal from a biased Hamiltonian that incorporates reliable assumptions about the final ground state. Our simulations show that this method yields a larger average gap throughout the whole computation, which results in an increased robustness of the overall annealing process.