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quant-ph2026
Quantum Graph Convolutional Networks: Implementation and Trainability Analysis
Paul San Sebastian Sein, Theodor Iosif, Tilen G. Limbäck-Stokin +2
Graph Neural Networks (GNNs) achieve state-of-the-art performance on graph-structured data, but training and inference on large graphs are often bottlenecked by memory constraints…
cs.LG2026
The Limits of Binding in Dual Encoders
Kin Ian Lo
Dual-encoder models such as CLIP score an image-caption pair by a single inner product of two independently computed unit vectors, and fail at binding, often scoring near chance wh…