Neural networks have been actively explored for quantum state tomography (QST) due to their favorable expressibility. To further enhance the efficiency of reconstructing quantum states, we explore the similarity between language modeling and quantum state tomography and propose an attention-based QST method that utilizes the Transformer network to capture the correlations between measured results from different measurements. Our method directly retrieves the density matrices of quantum states from measured statistics, with the assistance of an integrated loss function that helps minimize the difference between the actual states and the retrieved states. Then, we systematically trace different impacts within a bag of common training strategies involving various parameter adjustments on the attention-based QST method. Combining these techniques, we establish a robust baseline that can efficiently reconstruct pure and mixed quantum states. Furthermore, by comparing the performance of three popular neural network architectures (FCNs, CNNs, and Transformer), we demonstrate the remarkable expressiveness of attention in learning density matrices from measured statistics.

翻译：神经网络因其优越的表达能力而被积极用于量子态层析成像研究。为进一步提升重构量子态的效能，我们探索了语言建模与量子态层析成像之间的相似性，提出了一种基于注意力的量子态层析成像方法，利用Transformer网络捕捉不同测量结果之间的相关性。该方法借助集成损失函数（该函数有助于最小化实际状态与重构状态之间的差异），直接从测量统计数据中提取量子态的密度矩阵。随后，我们系统性地追踪了一系列常见训练策略（涉及多种参数调整）对基于注意力的量子态层析成像方法的不同影响。结合这些技术，我们建立了一个稳健的基线模型，能够高效重构纯态和混合量子态。此外，通过比较三种主流神经网络架构（全连接网络、卷积神经网络和Transformer）的性能，我们证明了注意力机制在学习测量统计数据中密度矩阵方面具有卓越的表达能力。