超越训练范围的连续变量系统外推量子误差缓解
Extrapolative Quantum Error Mitigation in Continuous-Variable Systems beyond the Training Horizon
AI总结：
本文提出基于时间条件Swin Transformer的外推量子误差缓解框架，通过自适应层归一化嵌入演进时间，实现对连续变量系统中噪声的高效缓解。
摘要：
连续变量（CV）量子系统为量子信息处理提供了灵活的平台，在其中量子态可以表示在四阶相空间中。在实际实现中，环境噪声，主要表现为光子损失和退相干，逐渐劣化这些状态。基于机器学习的量子误差缓解（QEM）最近出现作为一种有前途的方法来抑制此类噪声；然而，现有方法通常局限于训练范围，并需要覆盖整个演化的训练数据，这在实验上是具有挑战性的。在这里，我们介绍了一个基于时间条件Swin Transformer的外推量子误差缓解框架。通过显式嵌入演进时间的方式，利用自适应层归一化，模型学习了一个修正映射，该映射考虑了噪声的持续积累，同时捕捉到非局部相空间相关性。在马尔可夫和非马尔可夫噪声下的数值模拟显示，在长时间范围内实现了准确的状态恢复，而现有方法在此时会退化。我们的结果确立了外推QEM作为在CV量子系统中缓解噪声的实用途径，而无需大量训练数据。
英文摘要：
Continuous-variable (CV) quantum systems provide a versatile platform for quantum information processing, in which quantum states can be represented in the quadrature phase space. In realistic implementations, environmental noise, primarily photon loss and dephasing, progressively degrades these states. Machine-learning-based quantum error mitigation (QEM) has recently emerged as a promising approach to suppress such noise; however, existing methods are typically limited to the training horizon and require training data that cover the entire evolution, which is experimentally demanding. Here we introduce a framework for extrapolative quantum error mitigation based on a time-conditioned Swin Transformer. By explicitly embedding the evolution time via adaptive layer normalization, the model learns a correction map that accounts for the continuous accumulation of noise while capturing nonlocal phase-space correlations. Numerical simulations under both Markovian and non-Markovian noise demonstrate accurate state recovery in the long-time regime, where existing approaches deteriorate. Our results establish extrapolative QEM as a practical route to mitigating noise in CV quantum systems without exhaustive training data.
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