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) to execute a quantum circuit plays an important role in the overall execution time and error. Thus, it is important to reduce the cluster state depth. In this paper, we propose FMCC, a compilation framework that employs dynamic programming to efficiently minimize the cluster state depth. Experimental results on five representative quantum algorithms show that FMCC achieves 53.6%, 60.6%, and 60.0% average depth reductions in small, medium, and large qubit counts compared to the state-of-the-art MBQC compilation frameworks.

翻译：测量型量子计算（MBQC）是一种有前景的量子计算范式，通过对纠缠量子比特进行“单向”测量来执行计算。它广泛应用于光子量子计算（PQC），其中计算在光子簇态（即纠缠光子的二维网格）上进行。在基于MBQC的PQC中，执行量子电路所需的簇态深度（即单向测量的长度）对整体执行时间和误差具有重要影响。因此，减少簇态深度至关重要。本文提出FMCC，一种基于动态规划的编译框架，旨在高效最小化簇态深度。在五个代表性量子算法的实验结果表明，与最先进的MBQC编译框架相比，FMCC在小、中、大量子比特数下分别实现了53.6%、60.6%和60.0%的平均深度缩减。