论文标题
快速最佳结构发生器用于参数化量子电路
Fast optimal structures generator for parameterized quantum circuits
论文作者
论文摘要
当前的结构优化算法可针对各种量子算法（VQAS）的新任务优化量子电路的结构，而无需使用任何先前的经验，这是效率低下且耗时的。此外，量子门的数量是这些算法的高参数，这很难确定。在本文中，我们为VQA提出了一种快速的结构优化算法，该算法会自动确定量子门的数量，并直接在许多训练任务上使用元训练的图形分量自动编码器（VAE）直接生成新任务的最佳结构。我们还开发了一个元训练的预测指标，以滤除性能差的电路，以进一步加速算法。仿真结果表明，与最先进的算法（即DQAS）相比，我们的方法输出结构的运行时间较低，运行时间的速度快70倍。
Current structure optimization algorithms optimize the structure of quantum circuit from scratch for each new task of variational quantum algorithms (VQAs) without using any prior experience, which is inefficient and time-consuming. Besides, the number of quantum gates is a hyper-parameter of these algorithms, which is difficult and time-consuming to determine. In this paper, we propose a rapid structure optimization algorithm for VQAs which automatically determines the number of quantum gates and directly generates the optimal structures for new tasks with the meta-trained graph variational autoencoder (VAE) on a number of training tasks. We also develop a meta-trained predictor to filter out circuits with poor performances to further accelerate the algorithm. Simulation results show that our method output structures with lower loss and it is 70 times faster in running time compared to a state-of-the-art algorithm, namely DQAS.
