Readme
The library is organized with three main modules:
- dataset: Collect the quantum data and their types, as well as the tuning parameters to be used further in the pipeline.
- vae: Define the Variational Auto-Encoder (VAE) model, as well as a Trainer Wrapper that generates a representation of the phase space of quantum data.
- sr: Symbolic Regression (SR) methods, used to generate compact descriptors of the unknown clusters that appear in the learned representation.
The library also contains submodules which are/could be used in this pipeline:
- nn: Where some neural network architectures are defined.
- clutering: Where a clustering algorithm is implemented, which could be used to cluster the learned representation.
The use of these modules throughout the entire pipeline is summarised below:
The qdisc.vae module uses the qdisc.Dataset and neural networks from qdisc.nn to learn a representation. Then the qdisc.sr module uses the dataset (and, for some approaches, the VAE) to extract analytical expressions.