RESOURCES

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”

2025/06/23: Yue Ban “Efficient quantum agorithmics for learning and simulating many-body systems “

2025/07/14: Federico Hernán Holik “Simulation of genuine multipartite non-locality as a tool for benchmarking and calibrating quantum processors “

2025/11/24: Giulio Gasbarri “Do we need machine learning in quantum sensing?”

2025/12/01: Xuemei Gu “AI-driven discovery of new experiments and ideas in quantum physics”

Other Webinars

2023/11/17: Artur García. “Quantum Computation at BSC”

202024/12/19: Matías Bilkis “Tres problemas cuánticos, QML CVC y muchos desafíos
RIPAISC“

2025/04/06: Mirun Jarda ” #CVCWomeninScience2025″

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.

By Aran Oliveras

Quonvolutional JAX

High performance Quanvolutional Neural Networks with JAX and Flax. Pennylane tutorial.