Towards Efficient Ansatz Architecture for Variational Quantum Algorithms
- URL: http://arxiv.org/abs/2111.13730v1
- Date: Fri, 26 Nov 2021 19:41:35 GMT
- Title: Towards Efficient Ansatz Architecture for Variational Quantum Algorithms
- Authors: Anbang Wu, Gushu Li, Yuke Wang, Boyuan Feng, Yufei Ding, Yuan Xie
- Abstract summary: Variational quantum algorithms are expected to demonstrate the advantage of quantum computing on noisy quantum computers.
Training such variational quantum algorithms suffers from gradient vanishing as the size of the algorithm increases.
We propose a novel training scheme to mitigate such noise-induced gradient vanishing.
- Score: 12.728075253374062
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: 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. We first introduce a new cost function of which the
gradients are significantly augmented by employing traceless observables in
truncated subspace. We then prove that the same minimum can be reached by
optimizing the original cost function with the gradients from the new cost
function. Experiments show that our new training scheme is highly effective for
major variational quantum algorithms of various tasks.
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