  Direct air capture of Carbon Dioxide is a technical solution that does not
rely on natural processes to capture CO2 from the atmosphere. In DAC, the
filter material is designed to specifically bind CO2 molecules. Hence a
high-capacity filter is sought. We aim to leverage the potential of quantum
computing to improve the filters used in DAC. Metal-Organic Frameworks (MOFs)
have high surface area and tunable pore sizes which makes them an attractive
material for gas storage and separation. Using the variational quantum
eigensolver (VQE) algorithm, we find the minimum of the potential energy
surface (PES) by first considering only the active site of the MOF (the metal
ion). For complex systems, we employ Density Matrix Embedding Theory and use
VQE as a fragment solver at the binding site. Techniques like
deparameterisation are used to minimise the count of trainable parameters. We
present results of ideal and noisy simulations as well as from a real hardware
device. Resources are estimated for MOFs unit cell. The findings from our study
demonstrates the potential of quantum computing to effectively perform quantum
simulations of strongly correlated fragments.
