The QAOA with Few Measurements

TL;DR

This paper investigates the performance of gradient-free classical optimizers for the QAOA, demonstrating successful optimization with minimal measurements ($N=1$) on a 16-qubit system, addressing challenges in slow-repetition quantum platforms.

Contribution

It introduces an analysis of dual annealing and natural evolution strategies for QAOA, showing effective optimization with very few measurements, suitable for slow quantum hardware.

Findings

Optimization feasible with $N=1$ measurement per point.

Successful application on 16-qubit systems.

Addresses slow-repetition quantum hardware constraints.

Abstract

The Quantum Approximate Optimization Algorithm (QAOA) was originally developed to solve combinatorial optimization problems, but has become a standard for assessing the performance of quantum computers. Fully descriptive benchmarking techniques are often prohibitively expensive for large numbers of qubits ($n \gtrsim 10$), so the QAOA often serves in practice as a computational benchmark. The QAOA involves a classical optimization subroutine that attempts to find optimal parameters for a quantum subroutine. Unfortunately, many optimizers used for the QAOA require many shots ($N \gtrsim 1000$) per point in parameter space to get a reliable estimate of the energy being minimized. However, some experimental quantum computing platforms such as neutral atom quantum computers have slow repetition rates, placing unique requirements on the classical optimization subroutine used in the QAOA in these systems. In this paper we investigate the performance of two choices of gradient-free classical optimizer for the QAOA - dual annealing and natural evolution strategies - and demonstrate that optimization is possible even with $N=1$ and $n=16$.