一句话：提出 FMCC 编译框架，利用动态规划与启发式最小化 MBQC 中簇态深度，显著减少执行时间与电路错误。
基于测量的量子计算（MBQC）是一种通过纠缠量子比特上的“单向”测量来执行计算的量子计算范式，广泛用于光子量子计算（PQC），其中计算在光子簇态（即二维纠缠光子网格）上进行。在基于 MBQC 的 PQC 中，簇态深度（即单向测量的长度）对整体执行时间和电路错误有重要影响。本文提出 FMCC，一个编译框架，采用动态规划结合启发式方法高效地最小化簇态深度。在六个量子应用上的实验结果表明，与当前最先进的 MBQC 编译方法相比，FMCC 在少量子比特数、中等量子比特数和大量子比特数场景下分别实现了平均 51.7%、57.4% 和 56.8% 的深度缩减。
Measurement-based quantum computing (MBQC) is a promising quantum computing paradigm that performs computation through "one-way" measurements on entangled quantum qubits. It is widely used in photonic quantum computing (PQC), where the computation is carried out on photonic cluster states (i.e., a 2-D mesh of entangled photons). In MBQC-based PQC, the cluster state depth (i.e., the length of one-way measurements) plays an important role in the overall execution time and circuit error. In this paper, we propose FMCC, a compilation framework that employs dynamic programming with heuristics to efficiently minimize the cluster state depth. Experimental results on six quantum applications show that FMCC achieves 51.7%, 57.4%, and 56.8% average depth reductions in small, medium, and large qubit counts compared to the state-of-the-art MBQC compilations.
@inproceedings{LiPMZYT24,
title = {FMCC: Flexible Measurement-based Quantum Computation over Cluster State},
author = {Yingheng Li and Aditya Pawar and Zewei Mo and Youtao Zhang and Jun Yang and Xulong Tang},
booktitle = {ASPLOS},
year = {2024},
doi = {10.1145/3622781.3674185}
}