可微最大似然噪声估计用于量子纠错
Differentiable Maximum Likelihood Noise Estimation for Quantum Error Correction
AI总结：
本文提出了一种可微最大似然估计框架，用于精确估计量子纠错中的噪声参数，通过梯度下降优化提升解码性能，实现更高效的量子纠错。
摘要：
准确的噪声估计对于容错量子计算至关重要，因为解码性能严重依赖于电路级噪声参数的准确性。在本文中，我们介绍了一个可微的最大似然估计（dMLE）框架，该框架能够实现精确、高效且完全可微的 syndrome 对数似然计算，允许通过梯度下降直接优化电路级噪声参数。利用精确的平面求解器处理重复码，并结合一种新的简化张量网络（TN）架构和优化的收缩路径寻找方法处理表面码，我们的方法即使对于距离为5的表面码，最多25轮，也能实现可处理且完全可微的似然评估。我们的方法在模拟中能够精确恢复底层错误概率，并在Google处理器的实验数据上，将重复码的逻辑错误率降低高达30.6(3)%，将表面码的逻辑错误率降低高达8.1(2)%，相比先前最先进的方法：相关性分析和强化学习（RL）方法。我们的方法通过直接最大化syndrome似然，产生可证明最优的、与解码器无关的错误先验，提供了一种强大的噪声估计和控制工具，以解锁当前和未来纠错量子处理器的全部潜力。
英文摘要：
Accurate noise estimation is essential for fault-tolerant quantum computing, since decoding per formance depends critically on the fidelity of circuit-level noise parameters. We introduce a differ entiable Maximum Likelihood Estimation (dMLE) framework that learns these parameters directly from experimental syndrome data using likelihood gradients, with no decoder in the training loop. Mapping syndrome likelihoods to statistical-mechanical models enables exact and efficient evalua tion via planar solvers for repetition codes and tensor networks (TNs) for surface codes, extending the exact treatment to code distance d = 5 with a small number of rounds, and, via spatial subsam pling combined with time-translation symmetry, to larger codes and larger round counts. Across four experimental superconducting datasets, dMLE reduces logical error rates by up to 30.6% for repetition codes. On the Sycamore d = 3 surface code, a TN decoder using the dMLE prior attains a logical error rate comparable to that reported for state-of-the-art AlphaQubit, using standard syndromes alone without auxiliary leakage data. On the larger d = 5 surface code the dMLE prior lowers the logical error rate of the TN and Tesseract decoders by 7.18% and 5.23% relative to a prior optimized by reinforcement learning; at d = 7 it lowers that of Tesseract by 4.56% and 4.83% in the X and Z bases.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
