@techreport{oai:ipsj.ixsq.nii.ac.jp:00217629,
 author = {Ningyi, Xie and Xinwei, Lee and Dongsheng, Cai and Nobuyoshi, Asai and Ningyi, Xie and Xinwei, Lee and Dongsheng, Cai and Nobuyoshi, Asai},
 issue = {7},
 month = {Mar},
 note = {The Quantum approximate optimization algorithm (QAOA) is a quantum algorithm that aims to produce approximate solutions for combinatorial optimization problems. The quantum circuit parameters of QAOA are optimized to find the optimal solution. Recent research found that, for the Max-cut problem, the optimal parameters of the QAOA circuit for one graph with small number of nodes can be used for another graph with larger number of nodes if their degrees have the same parity. In this paper, we come up with the conjecture of the condition of parameter transfer for larger circuit Depth QAOA, and numerically verify it for regular graphs., The Quantum approximate optimization algorithm (QAOA) is a quantum algorithm that aims to produce approximate solutions for combinatorial optimization problems. The quantum circuit parameters of QAOA are optimized to find the optimal solution. Recent research found that, for the Max-cut problem, the optimal parameters of the QAOA circuit for one graph with small number of nodes can be used for another graph with larger number of nodes if their degrees have the same parity. In this paper, we come up with the conjecture of the condition of parameter transfer for larger circuit Depth QAOA, and numerically verify it for regular graphs.},
 title = {Transferring Optimal QAOA Parameters Between Regular Graphs for Larger Circuit Depth},
 year = {2022}
}