20–24 May 2024
Europe/Rome timezone

QUANTUM TEA TENSOR NETWORK HACKATHON 2024

 

 

Padova 20-23 May, 2024

Padova 24 May, 2024 (MSCA AQTIVATE meeting, AQTIVATE fellows only) 

 

We invite you to join the tensor network-themed hackathon organized by the University of Padova and explore this fast-developing field. The event offers the opportunity to learn about tensor networks from first-year PhD students to early-stage researchers, and together we learn about the implementations and applications of various applications via Quantum TEA. We offer a variety of topics to connect tensor networks with surrounding fields, i.e., topics reach from condensed matter physics, to machine learning and high performance computing optimizations. This event follows the Quantum Computing School and the European Tensor Network School, both held in Padova the two weeks prior to the hackathon.

 

 

Hackathon setup

-In-person (no online mode available)
-Non-competitive, topic-based: you select preferred topics, and we form teams.
-Tutors from the quantum research group at the University of Padova
-Topics built around Quantum TEA; our Quantum TEA developers will be there to help you.
-Support to run on HPC clusters, e.g., on Cineca
 

Tutors

- Alice Pagano (PhD student, University of Ulm & University of Padova) 

 

- Davide Rattacaso (PostDoc, University of Padova) 

 

- Francesco Campaioli (PostDoc, University of Padova) 

 

- Gabriella Bettonte (Cineca) 

 

- Marco Ballarin (PhD student, University of Padova) 

 

- Marco Tesoro (PhD student, University of Padova) 

 

- Matteo Vandelli (Leonardo) 

 

- Nora Reinić (PhD student, University of Padova) 

 

- Peter Majcen (PhD student, University of Padova) 

 

- Ilaria Siloi (Research staff, University of Padova) 


 

 

Organizers

Simone Montangero, Padova University
Daniel Jaschke, University of Ulm and Padova University
Nora Reinić , Padova University
 

 

This school is part of the QCSC Quantum weeks in Padova. 
Have a look to the website for the other planned events!



 

                                         .                                                                            
                                                              
 

    


 ENGAGE is partly funded by the European Union’s Horizon 2020 Research and Innovation Programme under the Marie Sklodowska-Curie COFUND scheme with grant agreement No. 101034267