  In this study, we explore the potential of using quantum natural language
processing (QNLP) to inverse design metal-organic frameworks (MOFs) with
targeted properties. Specifically, by analyzing 150 hypothetical MOF structures
consisting of 10 metal nodes and 15 organic ligands, we categorize these
structures into four distinct classes for pore volume and $H_{2}$ uptake
values. We then compare various QNLP models (i.e. the bag-of-words, DisCoCat
(Distributional Compositional Categorical), and sequence-based models) to
identify the most effective approach to process the MOF dataset. Using a
classical simulator provided by the IBM Qiskit, the bag-of-words model is
identified to be the optimum model, achieving validation accuracies of 85.7%
and 86.7% for binary classification tasks on pore volume and $H_{2}$ uptake,
respectively. Further, we developed multi-class classification models tailored
to the probabilistic nature of quantum circuits, with average test accuracies
of 88.4% and 80.7% across different classes for pore volume and $H_{2}$ uptake
datasets. Finally, the performance of generating MOF with target properties
showed accuracies of 93.5% for pore volume and 89% for $H_{2}$ uptake,
respectively. Although our investigation covers only a fraction of the vast MOF
search space, it marks a promising first step towards using quantum computing
for materials design, offering a new perspective through which to explore the
complex landscape of MOFs.
