一句话：提出 QISMET，面向变分量子算法应对 NISQ 动态噪声景观中的瞬态错误。
在 NISQ 时代，量子系统具有动态特性，噪声源会随时间不断变化。来自动态 NISQ 噪声景观的瞬态错误难以理解，并且对迭代式和/或长时间运行的量子应用尤为有害，因此及时缓解这些错误对在真实应用中实现量子优势至关重要。针对变分量子算法（VQA）这一典型迭代/长时运行负载，论文提出 QISMET 来应对其动态噪声景观。所给摘要仅阐述了问题动机与重要性，未展开 QISMET 的具体方法、技术细节与实验结果；从标题可知其目标是在动态噪声环境下导航并缓解瞬态错误，以提升 VQA 的可靠性。
In the Noisy Intermediate Scale Quantum (NISQ) era, the dynamic nature of quantum systems causes noise sources to constantly vary over time. Transient errors from the dynamic NISQ noise landscape are challenging to comprehend and are especially detrimental to classes of applications that are iterative and/or long-running, and therefore their timely mitigation is important for quantum advantage in real-world applications.
@inproceedings{paperbot10011,
title = {Navigating the Dynamic Noise Landscape of Variational Quantum Algorithms with QISMET},
author = {Gokul Subramanian Ravi and Kaitlin Smith and Jonathan M. Baker and Tejas Kannan and Nathan Earnest and Ali Javadi-Abhari and Henry Hoffmann and Frederic T. Chong},
booktitle = {ASPLOS},
year = {2023},
doi = {10.1145/3575693.3575739}
}