Quantum Machine Learning Models Repository

Sebastian Molina - smolinad@unal.edu.co
Sergio Quiroga - squirogas@unal.edu.co
Supervisors: Fabio Gonzalez & Diego Useche.

This repository is a collection of quantum machine learning models implemented using various quantum computing frameworks. By the time being, the models were implemented on Tencents's TensorCircuit and IBM's Qiskit.

The webview of the notebooks can be seen here.

Model 1: Quantum Variational Circuit (Farhi & Neven)

• Description: Implements a quantum variational circuit for MNIST Digits classification, as per Farhi & Neven (2018).
• Directory: /mnist
• We introduce a companion Marimo notebook. Marimo is a new generation Python notebook that provides reactive and interactive data insights. The notebook is located here, and it can by run as follows:
  • Install marimo with pip install marimo.
  • Download the notebook, and in the downloads directory (or any chosen directory) run marimo edit nameofthenotebook.py.
  • A local host will promp in your default browser, just as Jupyter notebook.

Model 2: Quantum Support Vector Machine with $ZZ$-feature map

• Description: Implements a quantum version of a support vector machine for classification tasks.
• Directory: /qsvm

Model 3: Quantum Data Compression

• Description: Diego implemented a quantum data compresion algorithm based on this paper.
• Directory: /qsvm

Poster

• Description: A poster related to the motivations and objectives of this repository.
• Directory: https://smolinad.github.io/quantum_machine_learning/docs/poster

Paper

• Description: A paper related to the motivations and objectives of this repository.
• Directory: https://smolinad.github.io/quantum_machine_learning/docs/paper