In this study,we explore the potential of using quantum natural language processing(QNLP)for property-guided inverse design of metal-organic frameworks(MOFs)with targeted properties.Specifically,by analyzing 450 hypot...In this study,we explore the potential of using quantum natural language processing(QNLP)for property-guided inverse design of metal-organic frameworks(MOFs)with targeted properties.Specifically,by analyzing 450 hypothetical MOF structures consisting of 3 topologies,10 metal nodes and 15 organic ligands,we categorize these structures into four distinct classes for pore volume and CO_(2)Henry’s constant 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 88.6%and 78.0%for binary classification tasks on pore volume and CO_(2)Henry’s constant,respectively.Further,we developed multi-class classification models tailored to the probabilistic nature of quantum circuits,with average test accuracies of 92%and 80%across different classes for pore volume and CO_(2)Henry’s constant datasets.Finally,the performance of generating MOF with target properties showed accuracies of 97.75%for pore volume and 90%for CO_(2)Henry’s constant,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.展开更多
The rapid proliferation of multimodal misinformation on social media demands detection frameworks that are not only accurate but also robust to noise,adversarial manipulation,and semantic inconsistency between modalit...The rapid proliferation of multimodal misinformation on social media demands detection frameworks that are not only accurate but also robust to noise,adversarial manipulation,and semantic inconsistency between modalities.Existing multimodal fake news detection approaches often rely on deterministic fusion strategies,which limits their ability to model uncertainty and complex cross-modal dependencies.To address these challenges,we propose Q-ALIGNer,a quantum-inspired multimodal framework that integrates classical feature extraction with quantumstate encoding,learnable cross-modal entanglement,and robustness-aware training objectives.The proposed framework adopts quantumformalism as a representational abstraction,enabling probabilisticmodeling ofmultimodal alignment while remaining fully executable on classical hardware.Q-ALIGNer is evaluated on four widely used benchmark datasets—FakeNewsNet,Fakeddit,Weibo,and MediaEval VMU—covering diverse platforms,languages,and content characteristics.Experimental results demonstrate consistent performance improvements over strong text-only,vision-only,multimodal,and quantum-inspired baselines,including BERT,RoBERTa,XLNet,ResNet,EfficientNet,ViT,Multimodal-BERT,ViLBERT,and QEMF.Q-ALIGNer achieves accuracies of 91.2%,92.9%,91.7%,and 92.1%on FakeNewsNet,Fakeddit,Weibo,and MediaEval VMU,respectively,with F1-score gains of 3–4 percentage points over QEMF.Robustness evaluation shows a reduced adversarial accuracy gap of 2.6%,compared to 7%–9%for baseline models,while calibration analysis indicates improved reliability with an expected calibration error of 0.031.In addition,computational analysis shows that Q-ALIGNer reduces training time to 19.6 h compared to 48.2 h for QEMF at a comparable parameter scale.These results indicate that quantum-inspired alignment and entanglement can enhance robustness,uncertainty awareness,and efficiency in multimodal fake news detection,positioning Q-ALIGNer as a principled and practical content-centric framework for misinformation analysis.展开更多
基金National Research Foundation of Korea(Project Number RS-2024-00337004)for the financial support.
文摘In this study,we explore the potential of using quantum natural language processing(QNLP)for property-guided inverse design of metal-organic frameworks(MOFs)with targeted properties.Specifically,by analyzing 450 hypothetical MOF structures consisting of 3 topologies,10 metal nodes and 15 organic ligands,we categorize these structures into four distinct classes for pore volume and CO_(2)Henry’s constant 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 88.6%and 78.0%for binary classification tasks on pore volume and CO_(2)Henry’s constant,respectively.Further,we developed multi-class classification models tailored to the probabilistic nature of quantum circuits,with average test accuracies of 92%and 80%across different classes for pore volume and CO_(2)Henry’s constant datasets.Finally,the performance of generating MOF with target properties showed accuracies of 97.75%for pore volume and 90%for CO_(2)Henry’s constant,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.
基金Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2026R77)Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia,the Deanship of Scientific Research at Northern Border University,Arar,Saudi Arabia,through the project number NBU-FFR-2026-2248-02.
文摘The rapid proliferation of multimodal misinformation on social media demands detection frameworks that are not only accurate but also robust to noise,adversarial manipulation,and semantic inconsistency between modalities.Existing multimodal fake news detection approaches often rely on deterministic fusion strategies,which limits their ability to model uncertainty and complex cross-modal dependencies.To address these challenges,we propose Q-ALIGNer,a quantum-inspired multimodal framework that integrates classical feature extraction with quantumstate encoding,learnable cross-modal entanglement,and robustness-aware training objectives.The proposed framework adopts quantumformalism as a representational abstraction,enabling probabilisticmodeling ofmultimodal alignment while remaining fully executable on classical hardware.Q-ALIGNer is evaluated on four widely used benchmark datasets—FakeNewsNet,Fakeddit,Weibo,and MediaEval VMU—covering diverse platforms,languages,and content characteristics.Experimental results demonstrate consistent performance improvements over strong text-only,vision-only,multimodal,and quantum-inspired baselines,including BERT,RoBERTa,XLNet,ResNet,EfficientNet,ViT,Multimodal-BERT,ViLBERT,and QEMF.Q-ALIGNer achieves accuracies of 91.2%,92.9%,91.7%,and 92.1%on FakeNewsNet,Fakeddit,Weibo,and MediaEval VMU,respectively,with F1-score gains of 3–4 percentage points over QEMF.Robustness evaluation shows a reduced adversarial accuracy gap of 2.6%,compared to 7%–9%for baseline models,while calibration analysis indicates improved reliability with an expected calibration error of 0.031.In addition,computational analysis shows that Q-ALIGNer reduces training time to 19.6 h compared to 48.2 h for QEMF at a comparable parameter scale.These results indicate that quantum-inspired alignment and entanglement can enhance robustness,uncertainty awareness,and efficiency in multimodal fake news detection,positioning Q-ALIGNer as a principled and practical content-centric framework for misinformation analysis.
