arXiv: cs.AI preprintsInternational6 November 2026
What Does the Effective Dimension of a Hybrid Quantum Neural Network Actually Measure?
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arXiv:2610.14070v2 Announce Type: replace-cross Abstract: Hybrid quantum neural networks (QNNs) pass features from a classical backbone into a variational quantum circuit, and many studies log a scalar called the effective dimension as a sign of capacity or model quality. The effective dimension is usually defined as the participation ratio (PR) of the quantum Fisher information matrix (QFIM), but studies in practice log a PR of cheap parameter sensitivities instead, and the meaning of that scalar is rarely tested. We show that the logged scalar equals the PR of Pauli-Z output sensitivities, t
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