基于特征自适应提取的宽度量子态层析模型  

Broad quantum state tomography model based on adaptive feature extraction

作　　者：闫文杰 党东月 YAN Wenjie;DANG Dongyue(School of Artificial Intelligence,Hebei University of Technology,Tianjin 300401,China)

机构地区：[1]河北工业大学人工智能与数据科学学院,天津300401

出　　处：《计算机应用》2024年第12期3861-3866,共6页journal of Computer Applications

基　　金：国家自然科学基金资助项目(62201552)。

摘　　要：针对量子态层析(QST)面临的数据维度指数级增长的难题,提出一种基于特征自适应提取的宽度QST模型(AFE_BQST)。首先,引入特征自适应提取策略,从而避免权重的随机性生成导致的映射特征节点不确定的现象;其次,采用宽度学习系统(BLS),以一种非迭代的方式将输入的数据映射到更合适的特征空间,提取大容量数据特征;最后,在低维和高维量子态数据的情况下进行实验,采用平均保真度和运行时间这2个性能指标,将AFE_BQST与宽度QST(BQST)、基于深度神经网络的QST(D_QST)、基于卷积神经网络的QST(C_QST)和基于U形神经网络的QST(U_QST)模型对比。实验结果表明,在低维量子态小样本情况下,AFE_BQST与次优基线模型BQST相比,提升了0.045个百分点的平均保真度,且运行时间相当;在高维量子态大样本情况下,AFE_BQST与次优基线模型D_QST相比,提升了0.175个百分点的平均保真度,且减少了99%的运行时间。以上结果验证了AFE_BQST具有自适应提取量子态数据特征以及准确且高效地重构量子态数据的能力。Aiming at the problem of exponential growth of data dimension faced by Quantum State Tomography(QST),a Broad QST model based on Adaptive Feature Extraction(AFE_BQST)was proposed.Firstly,an adaptive feature extraction strategy was introduced to avoid the uncertainty of mapping feature nodes caused by random generation of weights.Secondly,Broad Learning System(BLS)was used to map the input data to a more appropriate feature space in a non-iterative way for feature extraction of large-capacity data.Finally,experiments were executed in the cases of low-and high-dimensional quantum state data to compare AFE_BQST with Broad QST(BQST),Deep neural network QST(D_QST),Convolutional neural network QST(C_QST)and U-shaped network QST(U_QST)models by using two indicators of average fidelity and running time.Experimental results show that in the case of small samples with low-dimensional quantum state,compared with the sub-optimal baseline model BQST,AFE_BQST improves the fidelity by 0.045 percentage points with the similar running time;in the case of large samples with high-dimensional quantum state,compared with the sub-optimal baseline model D_QST,AFE_BQST improves the fidelity by 0.175 percentage points with the running time reduced by 99%.The above results prove that AFE_BQST is able to extract quantum state data features adaptively and reconstruct quantum state data accurately and efficiently.

关 键 词：量子态层析 宽度学习系统 特征自适应提取 深度学习 保真度 

分 类 号：TP391.9[自动化与计算机技术—计算机应用技术]