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arXiv · 2312.10865

Minimizing Photonic Cluster State Depth in Measurement-Based Quantum Computing

Yingheng Li·Aditya Pawar·Zewei Mo·Youtao Zhang·Jun Yang·Xulong Tang
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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.

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Yingheng Li, Aditya Pawar, Zewei Mo, Youtao Zhang, Jun Yang, Xulong Tang. 2023-12-18. Minimizing Photonic Cluster State Depth in Measurement-Based Quantum Computing. https://arxiv.org/abs/2312.10865

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