标题： 稀疏泡利林德布洛特定理模型在全连接量子比特拓扑中的高效学习 显示英文标题

标题： Efficient learning of Sparse Pauli Lindblad models for fully connected qubit topology

摘要： 实现实际量子计算的挑战在于当前硬件规模和门保真度对错误和噪声的敏感性。 最近的研究表明，通过学习捕捉量子比特交叉干扰的基本噪声模型，可以推动实际量子计算的边界。 这已经在具有线性拓扑连接性的设备（即超导量子位设备）上仅使用稀疏泡利-林德布洛德模型来完成。 在本工作中，我们将这种噪声模型学习的理论要求扩展到具有全连接性的硬件（即离子阱设备）。 显示英文摘要

摘要： The challenge to achieve practical quantum computing considering current hardware size and gate fidelity is the sensitivity to errors and noise. Recent work has shown that by learning the underlying noise model capturing qubit cross-talk, error mitigation can push the boundary of practical quantum computing. This has been accomplished using Sparse Pauli-Lindblad models only on devices with a linear topology connectivity (i.e. superconducting qubit devices). In this work we extend the theoretical requirement for learning such noise models on hardware with full connectivity (i.e. ion trap devices).