发表机构
Shanghai Jiao Tong University（上海交通大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对远程态制备中的噪声问题，提出基于Transformer的量子态表征器TQSC，实现高保真度重建，并在MNIST传输任务中将误码率从50.34%降至零。
AI 中文摘要
量子通信支撑着安全信息处理和可扩展量子网络。特别是，远程态制备（RSP）能够实现高效的量子态传输，但在复杂噪声下准确估计目标态仍然具有挑战性。在此，我们提出一种基于Transformer的量子态表征器（TQSC）模型，用于噪声RSP实验。该模型从复杂散射环境中的噪声测量中重建实验制备的纯态和混合态光子偏振态，同时其注意力模式为测量可观测量之间的相关性提供了物理上有依据的见解。该方法在复杂散射和动态高斯噪声下实现了超过99.999%的平均估计器-目标态保真度，其鲁棒性和泛化性进一步通过Qiskit模拟的布洛赫球进行了检验。此外，在一个实际的MNIST图像传输任务中，对于保留态，经TQSC后处理后，解码误码率从50.34%降至零。TQSC模型能够在动态噪声下实现精确的层析表征，并提供物理上有依据的事后见解，为智能量子信息处理应用带来了希望。
英文摘要
Quantum communication underpins secure information processing and scalable quantum networks. In particular, remote state preparation (RSP) enables efficient quantum state transfer, but accurately estimating target states under complex noise remains challenging. Here, we propose a Transformer-based Quantum State Characterizer (TQSC) model for noisy RSP experiments. Our model reconstructs experimentally prepared pure and mixed photonic polarization states from noisy measurements in complex scattering environments, while its attention patterns provide physically grounded insights into correlations among the measured observables. The method achieves a mean estimator-target fidelity exceeding 99.999% under complex scattering and dynamic Gaussian noise, while its robustness and generalization are further examined using Qiskit-simulated Bloch-ball states. Furthermore, in a practical MNIST image transmission task with held-out states, the decoded bit error rate is reduced from 50.34% to zero after TQSC post-processing. The TQSC model enables accurate tomographic characterization under dynamic noise and provides physically grounded post-hoc insights, holding promise for intelligent quantum information processing applications.
