论文标题
有效的深度选择用于实施嘈杂的量子近似优化算法
Efficient Depth Selection for the Implementation of Noisy Quantum Approximate Optimization Algorithm
论文作者
论文摘要
近期量子设备上的噪声将不可避免地限制量子近似优化算法（QAOA）的性能。一个重要的结果是，QAOA的性能可能无法通过深度单调改善。特别是，在某个点可以找到最佳的深度，即噪声效应大于增加深度所带来的好处。在这项工作中，我们建议使用模型选择算法来识别一些正则化参数的最佳深度。数值实验表明，该算法可以有效地定位在松弛和发出噪音下的最佳深度。
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.
