Data repository for "Minimial-Risk Training Samples for QNN Training from Measurements"

Replication code and experiment result data for training Quantum Neural Networks with entangled data using one-dimensional projectors as observables. This is the version of the code that was used to generate the experiment results in the related publication. Experiments: - exp_inf_coeffvariation.py: Trains QNNs using training samples of varying Schmidt rank with fixed vector as Schmidt basis state. Varies the associated Schmidt coefficient. - exp_inf_random.py: Trains QNNs using random training data. Experiment results: - exp_inf_coeffvariation.zip and exp_inf_random.zip contain the raw experiment results for both experiments. - For each combination of controlled variables there is one directory containing the result of all 20 runs of the training process. - The results for each run are comprised of 3 files:   - [id]_losses.npy: The loss during the training process   - [id]_params.npy: The parameters of the QNN after the training process.   - [id]_V.npy: The trained QNN exported as a 2^4 * 2^4 unitary matrix. Analysis of data (data_extraction.py): - Computes means and standard deviation of various risk measures and saves the results Plots (plot_obs_risk.py): - Plots the risk w.r.t. the observable for both experiments based on the analysed data obtained from data_extraction.py. - Generates plot_coeffvariation.pdf and plot_random.pdf.

复现代码与实验结果数据集，用于训练基于纠缠数据的量子神经网络，观测量为单维投影算符。此代码版本用于生成相关出版物中的实验结果。实验内容：
- exp_inf_coeffvariation.py：使用固定向量作为施密特基态的施密特秩变化的训练样本，训练量子神经网络，并调整相关的施密特系数。
- exp_inf_random.py：使用随机训练数据训练量子神经网络。实验结果：
- exp_inf_coeffvariation.zip 和 exp_inf_random.zip 包含了两个实验的原始实验结果。
- 对于每个控制变量的组合，都有一个目录包含训练过程的所有20次运行的成果。
- 每次运行的成果由3个文件组成：
 - [id]_losses.npy：训练过程中的损失值
 - [id]_params.npy：训练后的量子神经网络的参数。
 - [id]_V.npy：训练好的量子神经网络以2^4 * 2^4酉矩阵的形式导出。
数据分析（data_extraction.py）：
- 计算各种风险度量指标的平均值和标准差，并保存结果。
绘图（plot_obs_risk.py）：
- 基于从 data_extraction.py 获得的已分析数据，绘制了关于观测量的风险图。
- 生成了 plot_coeffvariation.pdf 和 plot_random.pdf。