arXiv 2201.098662022-01quant-ph

含噪声量子处理器上基于稀疏 Pauli-Lindblad 模型的概率误差消除

Probabilistic error cancellation with sparse Pauli-Lindblad models on noisy quantum processors

Ewout van den Berg, Zlatko K. Minev, Abhinav Kandala, Kristan Temme

DOI：: 10.1038/s41567-023-02042-2

AI总结：

针对大型量子电路中相关噪声学习困难阻碍概率误差消除（PEC）实验实现的问题，提出可扩展的稀疏 Pauli-Lindblad 噪声模型学习与反转协议，在含串扰错误的超导量子处理器上演示了 PEC，为更大电路体积下实现无噪声观测量计算奠定基础。

摘要：

容错前量子计算机中的噪声会导致物理观测量的有偏估计。使用概率误差消除（PEC）可以获得准确的无偏估计，这是一种有效反转已充分表征的噪声通道的误差缓解技术。然而，学习大型量子电路中的相关噪声通道一直是一项重大挑战，并严重阻碍了实验实现。我们的工作提出了一种用于学习和反转稀疏噪声模型的实用协议，该模型能够捕获相关噪声并可扩展至大型量子设备。这些进展使我们能够在具有串扰错误的超导量子处理器上演示 PEC，从而为在更大电路体积下实现具有无噪声观测量的量子计算开辟了道路，提供了一个重要里程碑。

英文摘要：

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.

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