  Direct air capture (DAC) of carbon dioxide is a promising method for
mitigating climate change. Solid sorbents, such as metal-organic frameworks,
are currently being tested for DAC application. However, their potential for
deployment at scale has not been fully realized. The computational discovery of
solid sorbents is challenging, given the vast chemical search space and the DAC
requirements for molecular selectivity. Quantum computing can potentially
accelerate the discovery of solid sorbents for DAC by predicting molecular
binding energies. In this work, we explore simulation methods and algorithms
for predicting gas adsorption in metal-organic frameworks using a quantum
computer. Specifically, we simulate the potential energy surfaces of CO2, N2,
and H2O molecules at the Mg+2 metal center that represents the binding sites of
typical metal-organic frameworks. We apply the qubit-ADAPT-VQE technique to run
simulations on both classical computing and quantum computing hardware, and
achieve reasonable accuracy while maintaining hardware efficiency.
