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.

翻译：本研究探索了利用量子自然语言处理（QNLP）逆向设计具有目标性能的金属有机框架（MOF）的潜力。具体而言，通过分析由10种金属节点和15种有机配体构成的150种假设性MOF结构，我们将其按孔体积和H₂吸附值划分为四个不同类别。随后比较了多种QNLP模型（即词袋模型、DisCoCat（分布组合范畴语法）模型以及序列模型），以确定处理MOF数据集的最优方法。利用IBM Qiskit提供的经典模拟器，词袋模型被认定为最优模型，在二元分类任务中分别实现了85.7%（孔体积）和86.7%（H₂吸附）的验证准确率。此外，我们针对量子电路的概率特性开发了多类分类模型，在孔体积和H₂吸附数据集的各类别上分别获得了88.4%和80.7%的平均测试准确率。最后，在生成具有目标性能的MOF测试中，孔体积和H₂吸附的准确率分别达到93.5%和89%。尽管本研究仅覆盖了MOF广阔搜索空间的一小部分，但标志着向利用量子计算进行材料设计迈出了富有前景的第一步，为探索复杂MOF学科领域提供了全新视角。