MartinLNG/adversarial_training_mps

AI Summary: Implement a logging tool similar to the one used in the UMNN repository to track and record experimental results within the existing adversarial training pipeline for Matrix Product States (MPS). This involves understanding the UMNN logging implementation and adapting it to the current MPS project, potentially modifying existing scripts (mps_pretraining.py, sampling.py, discriminator_pretraining.py, adversarial_training.py) to integrate the logging functionality.

AI Summary: The task involves adding `__init__.py` files to each script folder within a project focused on adversarial training using Matrix Product States (MPS). Understanding the purpose of these files and writing the necessary code (likely minimal or empty files in this case) is required.

AI Summary: The task involves transferring code from exploratory Jupyter notebooks into dedicated Python scripts, organizing the code according to the project's directory structure (mps_pretraining.py, sampling.py, discriminator_pretraining.py, adversarial_training.py), and ensuring a clear separation between source code and experimental results.