Some code examples for the Quickstart 2025 Winter School on QML.
Makes sure you create a local virtual environment by suing uv or simply installing the requirements.txt file.
python3 -m venv .venv
source .venb/bin/activate
(.venv) pip install -r requirements.txt
In order to play around with squlearn, due to library incompatibilities, please create a second environment using the requirements-squlearn.txt file.
Under the notebooks folder, you will find a set of notebooks and some exercises to master some basic concepts in order to master the field.
- Section 0: Basic stuff around qubits and gates so that you can practice simulating using pure Numpy, specialized software like QuTip or Qiskit (among others). Also, a 101 on Adiabatic Quantum Computing.
- Section 1: We should understand that working with classical data requires some transformations. Going down the Quantum Embeddings and how to evaluate their fitness to the task at hand with metrics such as Target Alignment.
- Section 2: On dimensionality reduction and how to squeeze all the information that is available into a set of qubits we can easily simulate using our devices.
- Section 3: Understanding trainable embeddings and how these can be used to improve hybrid (quantum and classical) models like QSVMs.
- Section 4: Pure quantum approaches like Quantum Neural Networks.