论文标题
阴影蒸馏：量子误差减轻近期量子处理器的经典阴影
Shadow Distillation: Quantum Error Mitigation with Classical Shadows for Near-Term Quantum Processors
论文作者
论文摘要
在没有容错性的情况下，减轻量子信息处理设备中的错误尤为重要。抑制状态准备错误的一种有效方法是使用多个副本将理想的组件从嘈杂的量子状态提取。在这里，我们使用经典的阴影和随机测量值来规避以指数成本连贯访问多个副本的需求。我们使用数值模拟研究了资源的缩放，并发现与完整的状态断层扫描相比，开销仍然有利。我们在现实的实验约束下优化了测量资源，并将我们的方法应用于准备Greenberger-Horne-Zeilinger（GHz）状态的实验。除了改进稳定剂测量值外，对改进结果的分析还揭示了影响实验的错误的性质。因此，我们的结果提供了一种直接适用的方法，用于减轻近期量子计算机中的错误。
Mitigating errors in quantum information processing devices is especially important in the absence of fault tolerance. An effective method in suppressing state-preparation errors is using multiple copies to distill the ideal component from a noisy quantum state. Here, we use classical shadows and randomized measurements to circumvent the need for coherent access to multiple copies at an exponential cost. We study the scaling of resources using numerical simulations and find that the overhead is still favorable compared to full state tomography. We optimize measurement resources under realistic experimental constraints and apply our method to an experiment preparing Greenberger-Horne-Zeilinger (GHZ) state with trapped ions. In addition to improving stabilizer measurements, the analysis of the improved results reveals the nature of errors affecting the experiment. Hence, our results provide a directly applicable method for mitigating errors in near-term quantum computers.
