Analog Quantum Approximate Optimization Algorithm

TL;DR

This paper introduces an analog version of the Quantum Approximate Optimization Algorithm tailored for current quantum annealers, focusing on optimizing the schedule function to improve problem-solving within coherence time constraints.

Contribution

It proposes a novel analog algorithm that optimizes the schedule function for quantum annealers, enabling approximate solutions during their coherence time.

Findings

Potential to achieve quantum advantage with current hardware

Flexible schedule function parametrization via interpolation

Approximate solutions for optimization problems within coherence time

Abstract

We present an analog version of the quantum approximate optimization algorithm suitable for current quantum annealers. The central idea of this algorithm is to optimize the schedule function, which defines the adiabatic evolution. It is achieved by choosing a suitable parametrization of the schedule function based on interpolation methods for a fixed time, with the potential to generate any function. This algorithm provides an approximate result of optimization problems that may be developed during the coherence time of current quantum annealers on their way toward quantum advantage.