Transformer model improves quantum state measurement under noise
Noise-Robust Quantum State Characterization for Remote State Preparation with Deep Learning
Machine Learning
Summary
Measuring quantum states accurately is hard when there's lots of noise, especially for quantum communication. This work uses a Transformer-based deep learning model to better identify the quantum states even when the signals are noisy or scattered. The model achieves very high accuracy and helps reduce errors in practical tasks like transmitting images. It also provides insights into how the measurements relate to each other, helping people understand what's going on.
What this means in practice
- •For quantum communication engineers: Accurately reconstruct noisy quantum states for secure communication and network operations.
- •For quantum hardware developers: Improve noise tolerance when characterizing quantum photonic devices facing complex environmental conditions.
Authors
Bo Tang, Zixuan Liao, Hao Li, Yilin Yang, Jiani Lei, Zengya Li, Jing Qiu, Zhaohui Dong, Zhengyang Mao, Yuanhua Li, Yuanlin Zheng, Xianfeng Chen
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.