Efficient Depth Selection for the Implementation of Noisy Quantum Approximate Optimization Algorithm

TL;DR

This paper proposes a model selection algorithm to efficiently determine the optimal depth of QAOA circuits on noisy quantum devices, balancing performance gains and noise effects to improve practical implementation.

Contribution

It introduces a novel method for selecting the optimal QAOA depth using regularization-based model selection, addressing noise limitations in near-term quantum devices.

Findings

Algorithm effectively finds optimal depth under noise

Numerical experiments validate efficiency and accuracy

Balances depth benefits with noise effects

Abstract

Noise on near-term quantum devices will inevitably limit the performance of Quantum Approximate Optimization Algorithm (QAOA). One significant consequence is that the performance of QAOA may fail to monotonically improve with depth. In particular, optimal depth can be found at a certain point where the noise effects just outweigh the benefits brought by increasing the depth. In this work, we propose to use the model selection algorithm to identify the optimal depth with a few iterations of regularization parameters. Numerical experiments show that the algorithm can efficiently locate the optimal depth under relaxation and dephasing noises.