Real-time reconstruction of conditional quantum states from continuous measurement records is a fundamental requirement for quantum feedback control, yet standard stochastic master equation (SME) solvers require exact model specification, known system parameters, and are sensitive to parameter mismatch. While neural sequence models can fit these stochastic dynamics, the unconstrained predictors can violate physicality such as positivity or trace constraints, leading to unstable rollouts and unphysical estimates. We propose a Kraus-structured output layer that converts the hidden representation of a generic sequence backbone into a completely positive trace preserving (CPTP) quantum operation, yielding physically valid state updates by construction. We instantiate this layer across diverse backbones, RNN, GRU, LSTM, TCN, ESN and Mamba; including Neural ODE as a comparative baseline, on stochastic trajectories characterized by parameter drift. Our evaluation reveals distinct trade-offs between gating mechanisms, linear recurrence, and global attention. Across all models, Kraus-LSTM achieves the strongest results, improving state estimation quality by 7% over its unconstrained counterpart while guaranteeing physically valid predictions in non-stationary regimes.

翻译：从连续测量记录中实时重构条件量子态是实现量子反馈控制的基本要求，然而标准的随机主方程（SME）求解器需要精确的模型规格和已知的系统参数，并且对参数失配敏感。虽然神经序列模型可以拟合这些随机动力学，但无约束的预测器可能违反物理性，如正定性或迹约束，导致不稳定的推演和非物理的估计。我们提出了一种Kraus结构输出层，它将通用序列主干网络的隐藏表示转换为完全正定且保迹（CPTP）的量子操作，从而在构造上产生物理有效的状态更新。我们在多种主干网络（RNN、GRU、LSTM、TCN、ESN和Mamba）上实例化了该层，并以神经ODE作为比较基线，在参数漂移为特征的随机轨迹上进行实验。我们的评估揭示了门控机制、线性递归和全局注意力之间不同的权衡。在所有模型中，Kraus-LSTM取得了最强的结果，在非稳态区域中，其状态估计质量比其无约束对应模型提高了7%，同时保证了物理有效的预测。