Mitigating Noise-Induced Gradient Vanishing in Variational Quantum Algorithm Training

Understand

Variational quantum algorithms are expected to demonstrate the advantage of quantum computing on near-term noisy quantum computers.

• However, training such variational quantum algorithms suffers from gradient vanishing as the size of the algorithm increases.
• Previous work cannot handle the gradient vanishing induced by the inevitable noise effects on realistic quantum hardware.
• In this paper, we propose a novel training scheme to mitigate such noise-induced gradient vanishing.