论文标题
与QAOA的SDP初始化的暖启动桥接经典和量子
Bridging Classical and Quantum with SDP initialized warm-starts for QAOA
论文作者
论文摘要
我们在最大切割问题的背景下研究量子近似优化算法（QAOA）。近期（嘈杂）量子设备仅能（准确）在低回路深度下执行QAOA，而QAOA则需要相对较高的电路深度才能“参见”整个图。我们介绍了一个经典的预处理步骤，该步骤以偏置图中所有可能的剪辑的偏置叠加来初始化QAOA，称为温暖启动。特别是，我们的初始化是通过解决最大切割问题的低级半额定编程放松的解决方案来告知QAOA的。我们的实验结果表明，这种称为QAOA-WARM的QAOA变体能够在较小的训练时间（在QAOA的变异参数的优化阶段）上优于低回路深度的标准QAOA。我们提供了有关拟议框架性能的实验证据以及理论直觉。
We study the Quantum Approximate Optimization Algorithm (QAOA) in the context of the Max-Cut problem. Near-term (noisy) quantum devices are only able to (accurately) execute QAOA at low circuit depths while QAOA requires a relatively high circuit-depth in order to "see" the whole graph. We introduce a classical pre-processing step that initializes QAOA with a biased superposition of all possible cuts in the graph, referred to as a warm-start. In particular, our initialization informs QAOA by a solution to a low-rank semidefinite programming relaxation of the Max-Cut problem. Our experimental results show that this variant of QAOA, called QAOA-Warm, is able to outperform standard QAOA on lower circuit depths with less training time (in the optimization stage for QAOA's variational parameters). We provide experimental evidence as well as theoretical intuition on performance of the proposed framework.
