Machine learning and Quantum Computing for supersymmetric matrix models

by Enrico Rinaldi (Quantinuum)

Tuesday 12 Mar 2024, 13:00 → 14:00 Europe/Berlin

Supersymmetric quantum gauge theories are important mathematical tools in high energy physics. The wave function of such supersymmetric gauge theories is not known and it is challenging to obtain with traditional techniques such as Lattice Monte Carlo.

We employ a neural quantum state ansatz based on normalizing flows for the wave function of a supersymmetric matrix model and use a variational quantum Monte Carlo approach to discover the ground state of the system. Results are also compared to variational hybrid quantum-classical methods to ground state preparation using exact simulations. 

We discuss the difficulty of including bosonic particles and fermionic particles, as well as gauge degrees of freedom, which are challenges in common with the exploration of Lattice Field Theories in the Hamiltonian formulation.