Quantum seminars

Marco Ballarin and Giovanni Cataldi successfully defended their PhDs!Congratulation and all the best for your future endeavor!

Pietro attended the Annual Meeting of the European Working Group for scientific research on Quantum Computing for High Energy Physics applications (QC4HEP). The Event was hosted at CERN, coordinated by Ivano Tavernelli and Sofia Vallecorsa. Pietro chaired the session of the theory roundtable discussion on equilibrium quantum simulations.

https://www.youtube.com/watch?v=4uzIMFKbIL4 Even the complex subjects can be simplified and made fun. Check out the animated cartoon video explaining quantum qubit for the general public made by Alice Pagano! The characters, fox and squirrel, are voiced by Alice Pagano and Nora Reinić.

Alberto and Daniel joined, together with Lorenzo Borella from Padova’s BOOSTLab group, the “Tensor network and neural network implementation on FPGA” at CERN.They worked with their host, Enrique Rico Ortega, to better understand how tensor networks can be used in the Next Generation Triggers (NGT) project. After five days of workshop, where they shared different expertise, and a visit to the ATLAS experiment, they have gained the knowledge to tackle…

Francesco joins the second Quantum Energy Initiative workshop in Grenoble, presenting an overview of energy storage in many-body quantum systems and a recent work on charging precision using D-Wave’s Advantage QPU.

Discover more by visiting the official website.

Luka and Daniel visited the Consiglio Nazionale delle Ricerche (CNR) in Rome to explore how the next generation of Quantum TEA backends could look like. After three days of intense discussions with Massimo Bernaschi at the Istituto per le Applicazioni del Calcolo and thanks to Massimo’s patience, we have developed a common understanding where the bottlenecks of our tensor network simulations on the GPU are and how we…

Daniel presented during the first AI-INFN hackathon on quantum-inspired machine learning. The tensor network – that we know well from our quantum many-body systems – can also be applied to supervised machine learning. And our Quantum TEA software can be tuned to solve this type of problem as well!