量子动力学的鲁棒Lindbladian估计
Robust Lindbladian Estimation for Quantum Dynamics
AI总结：
针对Lindbladian模型拟合问题，提出对数搜索的算法改进并引入门集层析技术以提升对SPAM误差的鲁棒性，在模拟与真实超导量子比特硬件数据上验证了其有效性。
AI中文摘要：
我们重新审视了将Lindbladian模型拟合到量子过程层析输出的问题。一系列先前的理论工作通过考虑是否存在一个接近层析估计转移矩阵矩阵对数的Lindbladian生成元来处理该问题。该技术必须考虑矩阵对数的非唯一性，因此通常必须检查对数的多个分支。相比之下，据我们所知，所有在真实实验数据上的Lindbladian拟合的实际演示都避开了对数搜索，而是采用直接数值优化或针对特定实验实现的临时方法。在我们的工作中，我们引入了对数搜索的算法改进，证明它可以在实践中应用于与当前量子计算硬件相关的设置。此外，我们利用门集层析技术增强了Lindbladian拟合任务，以提高对状态制备与测量（SPAM）误差的鲁棒性，否则这些误差会掩盖对感兴趣过程底层模型的估计。我们使用采用一系列真实误差模型的模拟层析数据对技术进行了广泛基准测试，然后展示了它们在从真实超导量子比特硬件收集的层析数据上的应用。
英文摘要：
We revisit the problem of fitting Lindbladian models to the outputs of quantum process tomography. A sequence of prior theoretical works approached the problem by considering whether there exists a Lindbladian generator close to a matrix logarithm of the tomographically estimated transfer matrix. This technique must take into account the non-uniqueness of the matrix logarithm, so that in general multiple branches of the logarithm must be checked. In contrast, all practical demonstrations of Lindbladian fitting on real experimental data have to our knowledge eschewed logarithm search, instead adopting direct numerical optimisation or ad-hoc approaches tailored to a particular experimental realisation. In our work, we introduce algorithmic improvements to logarithm search, demonstrating that it can be applied in practice to settings relevant for current quantum computing hardware. We additionally augment the task of Lindbladian fitting with techniques from gate set tomography to improve robustness against state preparation and measurement (SPAM) errors, which can otherwise obfuscate estimates of the model underlying the process of interest. We benchmark our techniques extensively using simulated tomographic data employing a range of realistic error models, before demonstrating their application to tomographic data collected from real superconducting-qubit hardware.
