AI 中文总结
针对超导量子比特读出的难题，提出基于曼巴模型的多阶段鉴别器，能高效建模。轻量级模型读出保真度高且参数规模降49.6%，最优模型更好，不同输入下保持稳健，降低逻辑错误率，助力量子计算发展。
AI 中文摘要
可靠的量子比特读出是容错量子计算（FTQC）的关键瓶颈。在超导量子处理器中，读出操作容易出错且延迟高。在频率复用架构中这些挑战更严峻，相邻量子比特间信号串扰会显著降低读出保真度。现有基于机器学习的方法依赖前馈神经网络，存在参数规模大等问题。本文提出基于曼巴模型的多阶段量子比特状态鉴别器，能以线性复杂度进行高效序列建模。第一阶段进行初始状态鉴别，第二阶段识别并减轻弛豫诱导误差。轻量级模型几何平均读出保真度达0.906，优于现有最佳方法且参数规模减少49.6%；最优模型达0.911。两个模型在不同输入迹长度下都保持稳健，在500纳秒读出持续时间时保真度达0.893，在量子纠错中逻辑错误率比先前工作降低多达26%。
英文摘要
Reliable qubit readout is a critical bottleneck toward fault-tolerant quantum computing (FTQC). In superconducting quantum processors, readout operations are both error-prone and high-latency. These challenges become more severe in frequency-multiplexed architectures, where signal crosstalk among neighboring qubits significantly degrades readout fidelity. Existing machine learning (ML)-based approaches rely on feed-forward neural networks (FNNs) that suffer from large parameter sizes and lack an end-to-end network that jointly addresses relaxation errors and discriminates qubit states. In this work, we present a multi-stage qubit state discriminator based on the Mamba model, which enables efficient sequence modeling with linear complexity. The first stage performs initial state discrimination, followed by a refinement stage that identifies and mitigates relaxation-induced errors. Our lightweight model achieves a geometric mean readout fidelity of 0.906, outperforming the best-reported state-of-the-art method while reducing parameter size by 49.6%; our optimal model further reaches 0.911. Both models remain robust across varying input trace lengths, maintaining a high fidelity of 0.893 at readout durations as short as 500 $ns$, achieving up to a 26% reduction in logical error rate over prior work in quantum error correction (QEC).
CommentsAccepted at IEEE International Conference on Quantum Computing and Engineering (QCE) 2026
