AI RESEARCH
From Reachability to Learnability: Geometric Design Principles for Quantum Neural Networks
arXiv CS.LG
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ArXi:2603.03071v2 Announce Type: replace-cross Classical deep networks are effective because depth enables adaptive geometric deformation of data representations. In quantum neural networks (QNNs), however, depth or state reachability alone does not guarantee this feature-learning capability. We study this question in the pure-state setting by viewing encoded data as an embedded manifold in $\mathbb{C}P^{2^n-1}$ and analysing infinitesimal unitary actions through Lie-algebra directions. We.