Lifting框架：重复编码与神经解码提升超导量子中途测量精度
Lifting: Quantum State Discrimination Framework for Mid-Circuit Measurements
做量子纠错的看：中途测量老被噪声坑，Lifting用重复编码加神经解码在IBM真机上验证有效。
Lifting是面向中电路测量（MCM）的量子态判别（QSD）框架，针对超导硬件噪声导致的误判和测量次数累积后的信号漂移两个问题。它通过重复编码把目标量子比特与辅助量子比特的联合I-Q读出信号交给神经解码器，并用运行中原位校准让解码器适配执行时的实际条件。理论分析证明，在固定联合分布上保留额外通道不会增加Bayes风险，并给出基于重叠的上界收缩的充分条件。实验在IBM的三台超导处理器上测试了重复稳定子测量、校验子和隐形传态三类工作负载。
Lifting: Quantum State Discrimination Framework for Mid-Circuit Measurements
Quantum computing promises new capabilities beyond classical computation. Mid-circuit measurements (MCMs) enable error correction and outcome-dependent control, but their reliability is limited by noise on superconducting hardware. These operations depend on accurate quantum state discrimination (QSD), which assigns measured signals to state labels. Neural network-based (NN-based) QSD improves this assignment, yet its error remains bounded below by the Bayes risk of the available readout distributions. A complementary challenge is state deterioration as successive measurements accumulate and circuit execution advances, making static calibration less representative of the workload. We present Lifting, a QSD framework that addresses both challenges through repetition encoding, neural decoding, and in situ calibration. Encoding supplies joint in-phase and quadrature (I-Q) readout signals from the target and auxiliary qubits, while contextual calibration trains the decoder for the conditions encountered during execution. Our theoretical analysis shows that retaining additional channels from a fixed joint distribution cannot increase Bayes risk and establishes sufficient conditions for contraction of an overlap-based upper bound. Experiments across three IBM processors evaluate the framework on repeated stabilizer measurement, parity-check, and teleportation workloads.
