一句话：提出 Clapton，用 Clifford 辅助的 Hamiltonian 变换缓解 VQA 器件噪声，提升基态能量估计精度。
变分量子算法（VQA）有望在近期量子计算中实现量子优势，但 NISQ 设备的精度不足。Clapton 针对器件误差对 VQA 的影响，结合经典估计的已知良好量子态、器件噪声的可经典模拟模型以及 VQA 的变分原理，对问题的 Hamiltonian 施加变换，以在建模噪声下降低已知良好态的能量估计。其假设是：若这些态接近理想基态且噪声建模足够准确，该变换可显著减轻噪声对基态的影响，提高 VQA 解精度。Clapton 被实现为端到端从应用到设备的框架。在物理与化学多种科学应用上，使用噪声模型和真实量子设备评估，其平均 VQA 初始化改进为 1.7x 到 3.7x，最高达 13.3x，优于当前最优基线。
Variational quantum algorithms (VQAs) show potential for quantum advantage in the near term of quantum computing, but demand a level of accuracy that surpasses the current capabilities of NISQ devices. To systematically mitigate the impact of quantum device error on VQAs, we propose Clapton: Clifford-Assisted Problem Transformation for Error Mitigation in Variational Quantum Algorithms. Clapton leverages classically estimated good quantum states for a given VQA problem, classical simulable models of device noise, and the variational principle for VQAs. It applies transformations on the VQA problem's Hamiltonian to lower the energy estimates of known good VQA states in the presence of the modeled device noise. The Clapton hypothesis is that as long as the known good states of the VQA problem are close to the problem's ideal ground state and the device noise modeling is reasonably accurate (both of which are generally true), then the Clapton transformation substantially decreases the impact of device noise on the ground state of the VQA problem, thereby increasing the accuracy of the VQA solution. Clapton is built as an end-to-end application-to-device framework and achieves mean VQA initialization improvements of 1.7x to 3.7x, and up to a maximum of 13.3x, over the state-of-the-art baseline when evaluated for a variety of scientific applications from physics and chemistry on noise models and real quantum devices.
DOI 原文 · arXiv · PDF（开放获取） · DBLP
@inproceedings{SeifertDCR24,
title = {Clapton: Clifford Assisted Problem Transformation for Error Mitigation in Variational Quantum Algorithms},
author = {Lennart Maximilian Seifert and Siddharth Dangwal and Frederic T. Chong and Gokul Subramanian Ravi},
booktitle = {ASPLOS},
year = {2024},
doi = {10.1145/3622781.3674178}
}