Reproduction Code for: On Reducing the Amount of Samples Required for Training of QNNs

<p> Replication code for training Quantum Neural Networks using entangled datasets. <br />This is the version of the code that was used to generate the experiment results in the related publication. For future developments and discussion see <a href="https://github.com/UST-QuAntiL/entangled_qnn_training">the Github repository</a>. <p/> <p> <b>Experiments:</b><br /> <code>avg_rank_exp.py</code>: Experiments for training QNNs using training data of varying Schmidt rank<br /> <code>nlihx_exp.py</code>: Experiments for training QNNs using linearly dependent data<br /> <code>ortho_exp.py</code>: Experiments for training QNNs using orthogonal training data<br /> </p> <p> <b>Visualisation/Analysis of data (plots.py):</b><br /> - Generates plots for the experiments above either from the data in <code>experimental_results</code> or from the processed results (see Data).<br /> - Processes results to extract information from raw data in <code>experimental_results</code> (to change behavior see the function calls at the end of <code>plots.py</code>).<br /> </p> <p> <b>Data:</b><br /> The raw data for the experiments is available in <a href="https://doi.org/10.18419/darus-3442">the experiment dataset</a>. </p>