Bayesian Optimization for QAOA

TL;DR

This paper introduces a Bayesian optimization method for tuning QAOA parameters, significantly reducing quantum circuit calls and demonstrating robustness against noise, thus enhancing the efficiency of hybrid quantum-classical algorithms.

Contribution

It presents a novel Bayesian optimization approach for QAOA parameter tuning, improving efficiency and noise robustness compared to existing methods.

Findings

Reduces number of quantum circuit calls

Works well with slow circuit repetition rates

Robust against gate-level noise at low circuit depths

Abstract

The Quantum Approximate Optimization Algorithm (QAOA) adopts a hybrid quantum-classical approach to find approximate solutions to variational optimization problems. In fact, it relies on a classical subroutine to optimize the parameters of a quantum circuit. In this work we present a Bayesian optimization procedure to fulfil this optimization task, and we investigate its performance in comparison with other global optimizers. We show that our approach allows for a significant reduction in the number of calls to the quantum circuit, which is typically the most expensive part of the QAOA. We demonstrate that our method works well also in the regime of slow circuit repetition rates, and that few measurements of the quantum ansatz would already suffice to achieve a good estimate of the energy. In addition, we study the performance of our method in the presence of noise at gate level, and we find that for low circuit depths it is robust against noise. Our results suggest that the method proposed here is a promising framework to leverage the hybrid nature of QAOA on the noisy intermediate-scale quantum devices.