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”

2026/02/27: Laia Domingo: Quantum machine learning: Methods and applications across domains”

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.

By Yeray Corder

Quantum NeRF

An example of an output 3D model, in PLY format, of our Q-NeRF model is available here. The presentation slides of this project’s defense are also available in Google Slides.

By Yeray Cordero

Quantum Graph Neural Netork for solving the  Travelling Salesman Problem (TSP)

Based on the algorithm above, it illustrates its uses for the TSP..

By Oriol Balló

Practical introduction to JAX for QML with PennyLane

First steps for the use of JAX in the context of QML. It really improves computational time.

By Oriol Balló

Exploration of quantum image encodings

This tutorial summarizes the fundamental techniques to encode images in a quantum system (reviewd).

By Oriol Balló

Repository for Shallow IQP circuits and graph generation

We provide a kind introduction for a practical approach to the use of IQP circuits.