Abstract:

The search for practical applications of NISQ devices in quantum simulation has been challenging, particularly due to intrinsic noise and the large costs associated with achieving high accuracy. This is especially true in quantum chemistry, where efforts have mainly targeted the ground-state electronic structure problem, which remains prohibitively costly for any foreseeable NISQ algorithm. 

I will present alternative approaches for applying NISQ devices to electronic structure problems. I will discuss the potential and limitations of variational quantum algorithms in photochemistry. In this context, will introduce a noise-resilient algorithm for studying the topological properties of molecular spectra. I will also discuss how quantum devices can be employed to generate training data for physically-motivated (classical) machine learning models, particularly in the context of density functional theory.

Short Bio:

Stefano Polla studied physics in Italy and the Netherlands, with a focus on quantum computing and quantum optics. He earned his PhD from Leiden University in 2024, specializing in quantum algorithms for simulation under the supervision of T. E. O’Brien. During the last two years of his PhD, Stefano also worked part-time as a student researcher with the Google Quantum AI team. In 2023, he secured funding from Shell to support a small research team dedicated to quantum computing for chemistry. This project is conducted in collaboration with L. Visscher (VU Amsterdam) and is integrated into the Leiden Applied Quantum Algorithms group.