一句话：提出随机性感知编译框架，通过离线与在线优化及新型 IR，提升光子量子计算的可扩展性与效率。
光子平台在量子计算中具有广阔前景，但其原生 fusion 操作固有的概率性会给计算过程引入显著随机性，从而严重阻碍程序执行的可扩展性与效率。为同时应对这两个挑战，本文提出 OnePerc，一个随机性感知的编译框架。该方法创新性地结合离线优化与在线优化 pass，并设计了一种新颖的中间表示，作为连接两类优化的关键桥梁。通过全面评估，作者表明该框架能够以可扩展的方式显著优于当前最高效的基线编译器，为实现可扩展的光子量子计算开辟了新的可能性。
The photonic platform holds great promise for quantum computing. Nevertheless, the intrinsic probabilistic characteristic of its native fusion operations introduces substantial randomness into the computing process, posing significant challenges to achieving scalability and efficiency in program execution. In this paper, we introduce a randomness-aware compilation framework designed to concurrently achieve scalability and efficiency. Our approach leverages an innovative combination of offline and online optimization passes, with a novel intermediate representation serving as a crucial bridge between them. Through a comprehensive evaluation, we demonstrate that this framework significantly outperforms the most efficient baseline compiler in a scalable manner, opening up new possibilities for realizing scalable photonic quantum computing.
DOI 原文 · arXiv · PDF（开放获取） · DBLP
@inproceedings{ZhangRSKD24,
title = {OnePerc: A Randomness-aware Compiler for Photonic Quantum Computing},
author = {Hezi Zhang and Jixuan Ruan and Hassan Shapourian and Ramana Rao Kompella and Yufei Ding},
booktitle = {ASPLOS},
year = {2024},
doi = {10.1145/3620666.3651372}
}