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D. Picozzi

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#machine learning Preprint Sep 2026

The information geometry of large language models is shared, learned, and controllable

Large language models learn similar behaviours, yet it remains unclear what structure they share or how to change one behaviour without disturbing others. The Fisher-Rao geometry of next-token probabilities connects these questions: behaviour determines this geometry up to output-preserving symmetries, whereas activati...

D. Picozzi · 1 citation
Preprint Jul 2026

A hardware-efficient variational ansatz with an exact diagonal metric for real- and imaginary-time evolution and Haar sampling

A hardware-efficient $n-qubit ansatz, which parametrises states by a binary tree and whose Fubini-Study pullback metric is diagonal in closed form, which reaches reference-level accuracy with one to three orders of magnitude fewer two-qubit gates than leading alternatives.

D. Picozzi · 0 citations

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