Greedy parameter optimization for diabatic quantum annealing

TL;DR

This paper introduces a greedy parameter optimization method for diabatic quantum annealing that enhances success probability and reduces solution time, especially in noisy, short-annealing regimes, by variationally tuning a $y$-field term.

Contribution

The paper presents a simple greedy optimization procedure for tuning a $y$-field in quantum annealing, improving performance over traditional methods in ferromagnetic and spin-glass systems.

Findings

Outperforms traditional quantum annealing and simulated annealing in success probability.

Achieves better performance with shorter annealing times.

Non-stoquastic $\sigma^y$ term can be eliminated by a spin rotation, enabling experimental feasibility.

Abstract

A shorter processing time is desirable for quantum computation to minimize the effects of noise. We propose a simple procedure to variationally determine a set of parameters in the transverse-field Ising model for quantum annealing appended with a field along the $y$ axis. The method consists of greedy optimization of the signs of coefficients of the $y$-field term based on the outputs of short annealing processes. We test the idea in the ferromagnetic system with all-to-all couplings and spin-glass problems, and find that the method outperforms the traditional form of quantum annealing and simulated annealing in terms of the success probability and the time to solution, in particular in the case of shorter annealing times, achieving the goal of improved performance while avoiding noise. The non-stoquastic $\sigma^y$ term can be eliminated by a rotation in the spin space, resulting in a non-trivial diabatic control of the coefficients in the stoquastic transverse-field Ising model, which may be feasible for experimental realization.