Error-mitigation techniques such as probabilistic error cancellation and zero-noise extrapolation benefit from accurate noise models. The sparse Pauli-Lindblad noise model is one of the most successful models for those applications. In existing implementations, the model decomposes into a series of simple Pauli channels with one- and two-local terms that follow the qubit topology. While the model has been shown to accurately capture the noise in contemporary superconducting quantum processors for error mitigation, it is important to consider higher-weight terms and effects beyond nearest-neighbor interactions. For such extended models to remain practical, however, we need to ensure that they can be learned efficiently. In this work we present new techniques that accomplish exactly this. We introduce twirling based on Pauli rotations, which enables us to automatically generate single-qubit learning correction sequences and reduce the number of unique fidelities that need to be learned. In addition, we propose a basis-selection strategy that leverages graph coloring and uniform covering arrays to minimize the number of learning bases. Taken together, these techniques ensure that the learning of the extended noise models remains efficient, despite their increased complexity.

中文翻译：

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学习稀疏 Pauli-Lindblad 噪声模型的技术


误差缓解技术（如概率误差消除和零噪声外推）受益于精确的噪声模型。稀疏 Pauli-Lindblad 噪声模型是这些应用最成功的模型之一。在现有实现中，该模型分解为一系列简单的 Pauli 通道，其中包含遵循量子比特拓扑的单局部和双局部项。虽然该模型已被证明可以准确捕获现代超导量子处理器中的噪声以减少错误，但重要的是要考虑最近邻相互作用之外的更高权重的项和影响。然而，为了使这种扩展模型保持实用性，我们需要确保它们可以被有效地学习。在这项工作中，我们提出了实现这一目标的新技术。我们引入了基于 Pauli 旋转的旋转，这使我们能够自动生成单量子比特学习校正序列，并减少需要学习的唯一保真度的数量。此外，我们提出了一种基础选择策略，该策略利用图形着色和均匀覆盖数组来最大限度地减少学习基础的数量。综上所述，这些技术可以确保扩展噪声模型的学习仍然高效，尽管它们的复杂性增加。