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Barcelona Supercomputing Center

Academic institutioneurope · es
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Research library178linked papers
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Selected work

Representative Papers

Warm Starts, Cold States: Exploiting Adiabaticity for Variational Ground-States

Feb 05, 2026

This work addresses the challenge that variational quantum eigensolvers (VQE) often converge to local minima or suffer from barren plateaus in complex energy landscapes, hindering reliable preparation of many-body ground states. To overcome this, the authors propose an iterative strategy inspired by adiabatic evolution, which constructs a discretized deformation path of the Hamiltonian and tracks the ground-state manifold across a sequence of intermediate problems to guide VQE toward the target ground state. The method provides theoretical guarantees on trainability by avoiding regions where the spectral gap closes, thereby significantly enhancing the robustness and scalability of ground-state preparation. Numerical experiments demonstrate that the approach achieves stable convergence even in the presence of measurement noise, effectively circumventing the optimization pitfalls commonly encountered in conventional VQE implementations.

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Tensor-DTI: Enhancing Biomolecular Interaction Prediction with Contrastive Embedding Learning

Jan 09, 2026arXiv.org

This work addresses the limited expressiveness and generalizability of existing drug–target interaction (DTI) prediction models that rely on single-modality representations. The authors propose Tensor-DTI, a novel framework that, for the first time, integrates multimodal embeddings—comprising molecular graphs, protein language models, and binding site predictions—through contrastive learning. A siamese dual-encoder architecture is employed to model chemico-structural interaction features. Tensor-DTI significantly improves performance in distinguishing interacting from non-interacting pairs, outperforming state-of-the-art methods across multiple DTI benchmarks. It yields plausible hit distributions in billion-scale virtual screening against CDK2 and substantially reduces the cost of discovering high-affinity ligands for out-of-family targets. Moreover, the model demonstrates strong interpretability and cross-task extensibility, as evidenced by its applicability to protein–RNA and peptide–protein interactions.

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