Impact of Error Mitigation and Error Detection on Noise-induced Barren Plateau

Variational quantum algorithms (VQA) suffer from the noise-induced barren plateau (NIBP) problem during training process. In this scenario, the landscape of the cost function becomes exponentially flat due to noise. Both error mitigation (EM) and error detection codes (ED) with post-selection have demonstrated their efficacy in reducing the impact of noise on near-term quantum computers. A natural inquiry that arises is whether EM and ED can alleviate the NIBP issue. In this project, we assess the impact of various EM and ED methods on QAOA and report its approximation ratio in relation to the increase in noise level (number of layers in the circuit). We offer a theoretical analysis based on our proposed noise model and present experimental results obtained from IBM quantum hardware.