Noise-Robust Quantum State Characterization for Remote State Preparation with Deep Learning
Abstract
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.
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BibTeX
@misc{tang2026noiserobust,
title = {{Noise-Robust Quantum State Characterization for Remote State Preparation with Deep Learning}},
author = {Bo Tang and Zixuan Liao and Hao Li and Yilin Yang and Jiani Lei and Zengya Li and Jing Qiu and Zhaohui Dong and Zhengyang Mao and Yuanhua Li and Yuanlin Zheng and Xianfeng Chen},
howpublished = {arXiv},
year = {2026},
month = sep,
doi = {10.48550/arxiv.2609.20523},
eprint = {2609.20523},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2609.20523},
}
RIS
TY - UNPB
TI - Noise-Robust Quantum State Characterization for Remote State Preparation with Deep Learning
AU - Bo Tang
AU - Zixuan Liao
AU - Hao Li
AU - Yilin Yang
AU - Jiani Lei
AU - Zengya Li
AU - Jing Qiu
AU - Zhaohui Dong
AU - Zhengyang Mao
AU - Yuanhua Li
AU - Yuanlin Zheng
AU - Xianfeng Chen
PY - 2026
DA - 2026/09/19
PB - arXiv
DO - 10.48550/arxiv.2609.20523
UR - https://arxiv.org/abs/2609.20523
AB - 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.
ER -