一句话：提出基于 Pauli 中间表示的分块编译框架 Paulihedral，深度优化量子模拟内核。
量子模拟内核是许多量子程序中出现的超长门序列子程序，在低层门级难以进行大规模优化。本文提出 Paulihedral，一个分块（block-wise）编译器框架，通过挖掘高层程序结构与优化机会来深度优化该子程序。Paulihedral 首先采用一种新的 Pauli 中间表示，能够保留量子模拟内核中的高层语义与约束，从而使低门级难以实现的大规模优化自然可行。具体而言，作者提出两个与技术无关的指令调度 pass，以及两个与技术相关的代码优化 pass，协调编译器中电路综合、门消除与量子比特映射等阶段。实验表明，在近期超导量子处理器与未来容错量子计算机的广泛应用中，Paulihedral 均优于现有最先进的编译器基础设施。
The quantum simulation kernel is an important subroutine appearing as a very long gate sequence in many quantum programs. In this paper, we propose Paulihedral, a block-wise compiler framework that can deeply optimize this subroutine by exploiting high-level program structure and optimization opportunities. Paulihedral first employs a new Pauli intermediate representation that can maintain the high-level semantics and constraints in quantum simulation kernels. This naturally enables new large-scale optimizations that are hard to implement at the low gate-level. In particular, we propose two technology-independent instruction scheduling passes, and two technology-dependent code optimization passes which reconcile the circuit synthesis, gate cancellation, and qubit mapping stages of the compiler. Experimental results show that Paulihedral can outperform state-of-the-art compiler infrastructures in a wide-range of applications on both near-term superconducting quantum processors and future fault-tolerant quantum computers.
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@inproceedings{LiWSJDX22,
title = {Paulihedral: a generalized block-wise compiler optimization framework for Quantum simulation kernels},
author = {Gushu Li and Anbang Wu and Yunong Shi and Ali Javadi-Abhari and Yufei Ding and Yuan Xie},
booktitle = {ASPLOS},
year = {2022},
doi = {10.1145/3503222.3507715}
}