The 38th International Symposium on Lattice Field Theory

Bayesian Optimization for Variational Quantum Eigensolvers

28 Jul 2021, 13:00

15m

Oral presentation Algorithms (including Machine Learning, Quantum Computing, Tensor Networks) Algorithms (including Machine Learning, Quantum Computing, Tensor Networks)

Speaker

Giovanni Iannelli (Deutsches Elektronen-Synchrotron DESY)

Description

The Variational Quantum Eigensolver (VQE) is a hybrid quantum-classical algorithm used to find the spectrum of a Hamiltonian using the variational method. In particular, this procedure can be used to study LGT in the Hamiltonian formulation. Bayesian Optimization (BO) based on Gaussian Process Regression (GPR) is a powerful algorithm for finding the global minimum of the energy with a very low number of iterations. This work explores some available methods for BO and GPR, and proposes a setup that is specifically tailored to perform VQE with NISQ devices.

Presentation materials

Bayesian Optimization for Variational Quantum Eigensolvers

slides.pdf