Efficient Depth Selection for the Implementation of Noisy Quantum Approximate Optimization Algorithm

• URL: http://arxiv.org/abs/2207.04263v1
• Date: Sat, 9 Jul 2022 13:07:21 GMT
• Title: Efficient Depth Selection for the Implementation of Noisy Quantum Approximate Optimization Algorithm
• Authors: Yu Pan, Yifan Tong, Shibei Xue, Guofeng Zhang
• Abstract summary: Noise on near-term quantum devices will inevitably limit the performance of Quantum Approximate Optimization Algorithm (QAOA) We propose to use the model selection algorithm to identify the optimal depth with a few iterations of regularization parameters. Numerical experiments show that the algorithm can efficiently locate the optimal depth under relaxation and dephasing noises.
• Score: 2.7443146049647984
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Noise on near-term quantum devices will inevitably limit the performance of Quantum Approximate Optimization Algorithm (QAOA). One significant consequence is that the performance of QAOA may fail to monotonically improve with depth. In particular, optimal depth can be found at a certain point where the noise effects just outweigh the benefits brought by increasing the depth. In this work, we propose to use the model selection algorithm to identify the optimal depth with a few iterations of regularization parameters. Numerical experiments show that the algorithm can efficiently locate the optimal depth under relaxation and dephasing noises.

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