People

The Quantum TEA package is developed by researchers of Quantum Information and Matter group at the University of Padova, a team specialized in tensor network algorithms with application to quantum simulation and computing. Originally started in 2006 by Simone Montangero and his collaborators as a Density Matrix Renormalization Group (DMRG) library, the project has since evolved into a collection of state-of-the-art tensor network algorithms. Today, it includes both well-established methods and novel algorithms developed by the members of the group.

Quantum TEA developers team

Our contributors include professors, postdoctoral researchers, PhD and master’s students, and collaborators from other leading institutions.

Prof. Simone Montangero

Simone has started the library back in 2006 as DMRG library with his collaborators. Today, he wonders if any of the original lines of codes have survived the past 15 years.

Daniel Jaschke

Daniel works on the simulation of the Schrödinger equation for many-body system and works mostly with Quantum Green Tea and the lower-level libraries as well as on the CI/CD side of Quantum Tea.

Marco Ballarin

Marco works on the emulation of Quantum Computers through tensor network algorithms and is the main developer of Quantum Matcha Tea. He also works on the lower-level libraries and the Python interfaces.

Alice Pagano

Alice works mostly with the lower-level libraries as the Quantum Red Tea and on the simulation of open quantum systems. She develops as well the machine learning library Quantum Chai Tea.

Nora Reinić

Nora is working on the implementation and improvement of tensor network algorithms within the Quantum Green Tea library. Moreover, she developed the tree tensor operator framework for finite temperature simulations within Quantum Tea Leaves.

Lisa Zangrando

Lisa is an INFN Technologist and is working to provide our QuantumTEA emulator as a Cloud Service named QuantumTEA-as-a-Service.

Timo Felser

Timo has driven the development of the current tree tensor network and augmented tree tensor network simulation framework, which now is part of the Quantum Green Tea.

Davide Rattacaso

Davide specializes in simulating fictitious many-body systems and utilizing quantum-inspired Tensor Network dynamics to solve combinatorial optimization problems. He implements modeling terms for generic connected many-body operators within Quantum Tea Leaves.

Luka Pavesic

Bio

Luka works on the tree tensor networks and augmented tree tensor network parts of Quantum Green Tea.

Sara Marzella

Sara is working together with the Quantum TEA team on the HPC integration on Cineca clusters.

Daniele Ottaviani

Daniele is working together with the Quantum TEA team on the HPC integration on Cineca clusters.

Gabriella Bettonte

Gabriella is working together with the Quantum TEA team on the HPC integration on Cineca clusters.

Asmita Datta

Asmita is contributing to the development of a tensor network based quantum computing (QC) emulator suited for a High Performance Computing environment and developing benchmarks for the QC platform by implementing lattice gauge theories (LGTs).

Ferdinand Tschirsich

Ferdinand has developed the Abelian symmetry tensor library together with many other low-level modules of the Quantum Tea library.

Rocco Borač

Rocco is currently working on developing a parallel tree tensor network algorithm for the Quantum Tea Leaves library. He is also involved in writing a software paper about the Quantum Green Tea library.

Andrea De Girolamo

Andrea is working on the Quantum Matcha Tea library, focusing on the implementation of noise models to emulate realistic quantum computers. For this purpose, he is adapting tensor-network ansätze for open quantum systems from Quantum Tea Leaves to simulate quantum channels in Matcha Tea.

Guillermo Muñoz Menés

Guillermo mainly contributes to the Quantum Chai Tea library, focusing on the development of quantum-inspired generative machine learning techniques. His work aims to apply machine learning techniques for optimal control and noise modeling.

Alberto Coppi

Alberto studies the application of tensor network algorithms to high-energy physics experiments. He is currently working on the machine learning library Quantum Chai Tea, optimizing it for low-latency hardware.

Marzolo street 8, 35131 Padova - Italy | C.F. 84001850589 | Contact: quantumtea@lists.infn.it

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