  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.

Quantum Physics;Hardware Architecture