高效且抗SPAM的无拟设Lindbladian学习
Efficient and SPAM-Robust Ansatz-Free Lindbladian Learning
AI总结：
提出一种基于Bell采样的无拟设Lindbladian学习算法，具有多项式时间经典后处理，并首次引入抗SPAM噪声的协议，可学习稀疏Lindbladians的规范无关分量。
AI中文摘要：
描述开放系统的动力学对于容错量子计算至关重要。在马尔可夫假设下，我们可以通过Lindbladian来刻画耗散动力学。利用Bell采样，我们提供了一种高效、无拟设的Lindbladian学习算法，具有多项式时间的经典后处理。鉴于近期设备上状态制备和测量（SPAM）噪声的普遍性，我们还引入了第一个高效的抗SPAM协议，能够在存在常数阶SPAM误差的情况下，以任意精度学习稀疏Lindbladians的规范无关分量。在此过程中，我们首次严格刻画了噪声Lindbladian学习中的规范自由度，精确识别了在SPAM噪声下哪些分量仍然可学习。
英文摘要：
Describing the dynamics of open systems is essential for fault-tolerant quantum computation. Under Markovian assumptions, we can characterize dissipative dynamics via the Lindbladian. Using Bell sampling, we provide an efficient, ansatz-free Lindbladian learning algorithm with polynomial-time classical postprocessing. Motivated by the prevalence of state preparation and measurement (SPAM) noise on near-term devices, we also introduce the first efficient SPAM-robust protocol capable of learning the gauge-independent components of sparse Lindbladians to arbitrary precision in the presence of constant-order SPAM error. In doing so, we provide the first rigorous characterization of the gauge degrees of freedom in noisy Lindbladian learning, precisely identifying which components remain learnable under SPAM noise.
