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Multimodal deep learning for automated atomic qubits fabrication in silicon

Yuwei Cui, Kunrong Wu, Ping Wu, Luyan Yang et al. • Machine Learning: Science and Technology (2026)

Donor-based spin qubits in silicon are a promising platform for scalable quantum computing due to their long coherence times and high-fidelity gate operations. A viable path for fabricating donor qubit arrays with atomic precision is scanni...

DOAJ Open Access (DOAJ) DOI: 10.1088/2632-2153/ae8e30

Understanding tourist engagement with traditional dwellings through multimodal deep learning: a case study of the Gao Family Compound, Xi’an

Liyue Wu, Qinchuan Zhan, Yanjun Li • Humanities & Social Sciences Communications (2026)

Abstract This study applies a multimodal deep learning approach to examine tourist engagement with traditional architectural heritage. The Gao Family Compound in Xi’an serves as a representative case. Social-media texts and images from Weib...

DOAJ Open Access (DOAJ) DOI: 10.1057/s41599-026-07879-5

Multimodal Deep Learning

Jiquan Ngiam, Aditya Khosla, Mingyu Kim, Juhan Nam et al. (2011)

Deep networks have been successfully applied to unsupervised feature learning for single modalities (e.g., text, images or audio). In this work, we propose a novel application of deep networks to learn features over multiple modalities. We ...

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