Analog QAOA with Bayesian Optimisation on a neutral atom QPU

TL;DR

This paper demonstrates an analog implementation of QAOA on a neutral atom quantum processor, using Bayesian Optimization to efficiently find solutions to the Maximum Independent Set problem despite hardware noise and resource constraints.

Contribution

It introduces a novel analog QAOA approach on neutral atoms combined with Bayesian Optimization for effective parameter tuning under noisy conditions.

Findings

Successful experimental implementation on Pasqal's quantum processor

Effective noise mitigation strategies improve solution quality

Limited measurements enable quick convergence to solutions

Abstract

This study explores the implementation of the Quantum Approximate Optimisation Algorithm (QAOA) in its analog form using a neutral atom quantum processing unit to solve the Maximum Independent Set problem. The analog QAOA leverages the natural encoding of problem Hamiltonians by Rydberg atom interactions, while employing Bayesian Optimisation to navigate the quantum-classical parameter space effectively under the constraints of hardware noise and resource limitations. We evaluate the approach through a combination of simulations and experimental runs on Pasqal's first commercial quantum processing unit, Orion Alpha, demonstrating effective parameter optimisation and noise mitigation strategies, such as selective bitstring discarding and detection error corrections. Results show that a limited number of measurements still allows for a quick convergence to a solution, making it a viable solution for resource-efficient scenarios.