RESOURCES – QML-CVC group

QML-CVC Materials and resources

Our team produces scientific publications, open GitHubs and tutorials. This serves as support materials for the research results and consolidates a repository of the algorithms developed. You can check our open production in the following link.

QML-CVC Seminars

Seminars organised by the group, provided by relevant colleagues from fields related to quantum machine learning.

2025/01/13: Gorka Muñoz Gil “Quantum circuit synthesis with diffusion models”

2025/02/03: Elia Bertondo “Introduction to superconducting qubits and experimental activities”

2025/02/17: Joseph Bowles “Scalable training of large quantum machine learning models”

2025/03/03: Supanut Thanasilp “Quantum machine learning and the journey to its scalability”

2025/04/07: Enrico Rinaldi “Denoising quantum circuits: from optimal circuit to mitigated results using AI” (NOT RECORDED, ONLY LIVE SESION)

2025/05/05: Aleksandr Berezutskii “Tensor Networks for Quantum Computing and beyond”

2025/05/12: Paula García Molina “Quantum Computing and Quantum inspired numerical methods”

2025/05/19: Nicolás Gigena “A brief summary of quantum correlations”

2025/06/02: Carlos Bravo Prieto “Understanding quantum machine learning also requires rethinking generalization”

QML-CVC GitHubs

These are the software githubs used in our scientific contributions

By Matias Bilkis

A kind Introduction to Quantum Machine Larning (QML)

This is a Hands-on on Quantum Machine Learning (QML) using Python libraries.

By Yeray Cordero

Quantum Reinforcement Learning for Quantum Graph Solutions

In this tutorial, we implement a specific approach to quantum graphs to solve the Traveler Salesman Problem.

Other contributions

2024/12/19: Matías Bilkis “Tres problemas cuánticos, QML CVC y muchos desafíos
RIPAISC“

2025/01/05: Matías Bilkis “Quantum Machine Learning, Music, Existence, Meaning of Life, & More | Dr. Matias Bilkis. Genesis of Tomorrow“