一句话：提出 QUEST，通过电路划分与近似合成系统生成量子电路近似，减少 CNOT 门数并保持高保真度。
量子电路中的 CNOT 门数量直接影响执行长度与噪声敏感度。QUEST 提出一种系统化生成量子电路近似的过程：采用电路划分保证可扩展性，并用近似合成缩短电路；通过运行近似空间中具有代表性的关键样本提升保真度，同时对近似误差上界进行推理。评估表明，采用“相异”近似可在保持与原电路接近保真度的同时，在理想系统上把 CNOT 门数减少 30%–80%，并降低现有及近期量子系统中噪声的影响。
We present QUEST, a procedure to systematically generate approximations for quantum circuits to reduce their CNOT gate count. Our approach employs circuit partitioning for scalability with procedures to 1) reduce circuit length using approximate synthesis, 2) improve fidelity by running circuits that represent key samples in the approximation space, and 3) reason about approximation upper bound. Our evaluation results indicate that our approach of "dissimilar" approximations provides close fidelity to the original circuit. Overall, the results indicate that QUEST can reduce CNOT gate count by 30-80% on ideal systems and decrease the impact of noise on existing and near-future quantum systems.
@inproceedings{PatelYIJT22,
title = {QUEST: systematically approximating Quantum circuits for higher output fidelity},
author = {Tirthak Patel and Ed Younis and Costin Iancu and Wibe de Jong and Devesh Tiwari},
booktitle = {ASPLOS},
year = {2022},
doi = {10.1145/3503222.3507739}
}