论文标题
在中性原子量子处理器上解决组合图问题的有效协议
Efficient protocol for solving combinatorial graph problems on neutral-atom quantum processors
论文作者
论文摘要
在中性原子平台上，制备特定量子状态通常是通过脉冲成型来实现的，即通过优化与系统相关的汉密尔顿的时间依赖性。这个过程可能非常昂贵，因为它需要多次在量子处理器中对最终状态进行采样。因此，确定良好的脉搏以及良好的嵌入来解决特定的组合图问题是模拟方法中最重要的瓶颈之一。在这项工作中，我们提出了一种新的协议，以解决结合变异模拟量子计算和机器学习的硬组合图问题。我们的数值模拟表明，所提出的协议可以大大减少在量子设备上运行的迭代次数。最后，我们通过估计相关的Q得分来评估所提出的方法的质量，该标准旨在基准测试QPU。
On neutral atom platforms, preparing specific quantum states is usually achieved by pulse shaping, i.e., by optimizing the time-dependence of the Hamiltonian related to the system. This process can be extremely costly, as it requires sampling of the final state in the quantum processor many times. Hence, determining a good pulse, as well as a good embedding, to solve specific combinatorial graph problems is one of the most important bottlenecks of the analog approach. In this work, we propose a novel protocol for solving hard combinatorial graph problems that combines variational analog quantum computing and machine learning. Our numerical simulations show that the proposed protocol can reduce dramatically the number of iterations to be run on the quantum device. Finally, we assess the quality of the proposed approach by estimating the related Q-score, a recently proposed metric aimed at benchmarking QPUs.
