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

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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>.
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<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 />
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<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 />
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<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>.
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