Variational quantum eigensolver techniques for simulating carbon monoxide oxidation

Abstract

The variational quantum eigensolver is a quantum-classical algorithm used to solve optimisation problems in machine learning but demonstrates limitations when applied to simulations of large molecules. Here, the authors explore the use of adaptive variational algorithms and demonstrate how they can be used to improve performance when simulating molecules participating in carbon monoxide processes.