Minimizing Photonic Cluster State Depth in Measurement-Based Quantum Computing
Abstract
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.
Cite
@article{arxiv.2312.10865,
title = {Minimizing Photonic Cluster State Depth in Measurement-Based Quantum Computing},
author = {Yingheng Li and Aditya Pawar and Zewei Mo and Youtao Zhang and Jun Yang and Xulong Tang},
journal= {arXiv preprint arXiv:2312.10865},
year = {2023}
}