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Can Error Mitigation Improve Trainability of Noisy Variational Quantum Algorithms?
March 11, 2024, 4:42 a.m. | Samson Wang, Piotr Czarnik, Andrew Arrasmith, M. Cerezo, Lukasz Cincio, Patrick J. Coles
cs.LG updates on arXiv.org arxiv.org
Abstract: Variational Quantum Algorithms (VQAs) are often viewed as the best hope for near-term quantum advantage. However, recent studies have shown that noise can severely limit the trainability of VQAs, e.g., by exponentially flattening the cost landscape and suppressing the magnitudes of cost gradients. Error Mitigation (EM) shows promise in reducing the impact of noise on near-term devices. Thus, it is natural to ask whether EM can improve the trainability of VQAs. In this work, we …
abstract algorithms arxiv cost cs.lg error however landscape near noise quant-ph quantum quantum advantage studies type
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