论文标题
贝叶斯优化QAOA
Bayesian Optimization for QAOA
论文作者
论文摘要
量子近似优化算法（QAOA）采用了一种混合量子古典方法，以找到变异优化问题的近似解决方案。实际上，它依靠经典的子例程来优化量子电路的参数。在这项工作中，我们提出了一个贝叶斯优化程序，以实现此优化任务，并根据其他全球优化器进行了研究。我们表明，我们的方法可以大大减少量子电路的呼叫数量，量子电路通常是QAOA中最昂贵的部分。我们证明，我们的方法在慢速电路重复速率方面也可以很好地奏效，并且很少有量子Ansatz的测量值足以实现能量的良好估计。此外，我们在门水平存在噪声的情况下研究方法的性能，并且发现对于低回路深度，它具有稳定性的噪声。我们的结果表明，此处提出的方法是一个有前途的框架，可以利用QAOA在嘈杂的中间量子设备上的杂种性质。
The Quantum Approximate Optimization Algorithm (QAOA) adopts a hybrid quantum-classical approach to find approximate solutions to variational optimization problems. In fact, it relies on a classical subroutine to optimize the parameters of a quantum circuit. In this work we present a Bayesian optimization procedure to fulfil this optimization task, and we investigate its performance in comparison with other global optimizers. We show that our approach allows for a significant reduction in the number of calls to the quantum circuit, which is typically the most expensive part of the QAOA. We demonstrate that our method works well also in the regime of slow circuit repetition rates, and that few measurements of the quantum ansatz would already suffice to achieve a good estimate of the energy. In addition, we study the performance of our method in the presence of noise at gate level, and we find that for low circuit depths it is robust against noise. Our results suggest that the method proposed here is a promising framework to leverage the hybrid nature of QAOA on the noisy intermediate-scale quantum devices.
