Nicola Lanata

Nicola Lanata is a tenure-track Assistant Professor at the Rochester Institute of Technology (RIT) in the School of Physics and Astronomy within the College of Science. He also holds a Visiting Scholar position at the Center for Computational Quantum Physics (CCQ) in New York. Previously, he served as an assistant professor at Aarhus University in Denmark and NORDITA in Sweden, and held a prestigious Dirac Fellowship at the National High Magnetic Field Laboratory.

Lanata earned his PhD in "Theory and Numerical Simulation in Condensed Matter Systems" from the International School for Advanced Studies (SISSA-ISAS) in Italy. His research focuses on the quantum-mechanical properties of strongly correlated materials, particularly their electronic structure, energy, chemical, and spectral properties. He develops and applies advanced many-body techniques and machine-learning methods to model and simulate these properties with greater accuracy and efficiency.

His recent publications reveal a strong focus on quantum embedding methods, particularly the ghost Gutzwiller approximation, and their application to strongly correlated systems. His work spans theoretical developments in many-body physics alongside computational implementations for real materials. A significant trend in his recent work involves the integration of machine learning techniques to overcome computational bottlenecks in quantum embedding theories.

Scientific Recognition

• Recipient of the Dirac Fellowship at the National High Magnetic Field Laboratory

Lanata actively mentors students and is currently recruiting MS students for research projects on enhancing computational methods for strongly correlated materials and mitigating noise in quantum algorithms. He collaborates extensively with researchers at Rutgers University and the Center for Computational Quantum Physics (CCQ), indicating a strong research network in the field of computational quantum physics.

His current research program includes two major projects: "Enhancing Accuracy and Computational Efficiency for Strongly Correlated Materials" which advances Ghost Gutzwiller Approximation methods, and "Mitigating Noise in VQE Algorithms" which applies probabilistic machine learning to improve quantum-mechanical simulations.