Towards Efficient Ansatz Architecture for Variational Quantum Algorithms
This addresses a critical bottleneck for training variational quantum algorithms on noisy quantum computers, offering a potential improvement for quantum computing applications.
The paper tackles the problem of gradient vanishing in variational quantum algorithms due to noise on near-term quantum hardware by proposing a novel training scheme with a new cost function using traceless observables, resulting in significantly augmented gradients and high effectiveness across various tasks.
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.