逻辑错误的时空缓解
Spacetime mitigation of logical errors
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中文总结 AI 辅助
本研究结合错误检测与概率性错误消除，提出时空泡利-林德布拉德表示及后选择微扰噪声模型，在IBM量子处理器上验证，将采样开销降低63倍。
中文摘要 AI 辅助
错误检测可以在比概率性错误消除（PEC）更低的采样成本下抑制噪声，但会留下残余的逻辑错误。我们将这两种方法结合起来，在降低PEC成本的同时缓解这些错误。我们引入了一种时空泡利-林德布拉德表示，并利用它从物理噪声和综合征信息中微扰地构建后选择逻辑噪声。单独未被检测到的故障在一阶贡献，而具有抵消综合征的检测到的故障则生成耦合不同时空位置的高阶项。为了调和后选择的非线性与PEC的线性，我们以未归一化的后选择映射来表述组合协议，仅在平均后才进行归一化。在ibm_aachen超导量子处理器上，Clifford实验验证了后选择噪声模型及其微扰标度。随后，我们使用22个数据和27个校验量子比特，演示了横向场伊辛动力学的一阶PEC错误检测。组合协议在六个Trotter步骤内恢复了统计不确定性范围内的平均磁化强度，并将推断的采样开销相对于无后选择的PEC降低了最多63倍。
英文摘要
Error detection can suppress noise at a lower sampling cost than probabilistic error cancellation (PEC), but leaves residual logical errors. We combine the two approaches to mitigate these errors while lowering the cost of PEC. We introduce a spacetime Pauli-Lindblad representation and use it to construct the post-selected logical noise perturbatively from physical noise and syndrome information. Individually undetected faults contribute at first order, while detected faults with canceling syndromes generate higher-order terms coupling distinct spacetime locations. To reconcile the nonlinearity of post-selection with the linearity of PEC, we formulate the combined protocol in terms of unnormalized post-selected maps, with normalization performed only after averaging. On the ibm_aachen superconducting quantum processor, Clifford experiments validate the post-selected noise model and its perturbative scaling. We then demonstrate error detection with first-order PEC for transverse-field Ising dynamics using 22 data and 27 check qubits. The combined protocol recovers the mean magnetization within statistical uncertainty through six Trotter steps and reduces the inferred sampling overhead by up to a factor of 63 relative to PEC without post-selection.
发表机构
- IBM Research（IBM 研究院）
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