论文标题
在嘈杂的量子处理器上使用稀疏的Pauli-Lindblad模型取消概率误差
Probabilistic error cancellation with sparse Pauli-Lindblad models on noisy quantum processors
论文作者
论文摘要
易于耐受的量子计算机中的噪声会导致物理可观察物的偏差估计。可以使用概率误差取消（PEC）获得准确的无偏差估计，这是一种有效逆转良好特征噪声通道的误差技术。但是，在大量子电路中学习相关的噪声通道一直是一个重大挑战，并且严重阻碍了实验实现。我们的工作提出了一种实用的协议，用于学习和颠倒稀疏的噪声模型，该模型能够捕获相关的噪声和尺度到大量子设备。这些进步使我们能够在带有串扰误差的超导量子处理器上演示PEC，从而为在较大电路量下使用无噪声可观察力的量子计算开辟量子计算方面提供了重要的里程碑。
Noise in pre-fault-tolerant quantum computers can result in biased estimates of physical observables. Accurate bias-free estimates can be obtained using probabilistic error cancellation (PEC), which is an error-mitigation technique that effectively inverts well-characterized noise channels. Learning correlated noise channels in large quantum circuits, however, has been a major challenge and has severely hampered experimental realizations. Our work presents a practical protocol for learning and inverting a sparse noise model that is able to capture correlated noise and scales to large quantum devices. These advances allow us to demonstrate PEC on a superconducting quantum processor with crosstalk errors, thereby providing an important milestone in opening the way to quantum computing with noise-free observables at larger circuit volumes.
