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R. Jomtarak

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#machine learning Preprint Nov 2026

Seeing Why: Exact Evidence Maps for Quantum and Optical Image Classifiers

When an image classifier decides, we can usually only estimate why. For quantum and all-optical classifiers that act linearly on amplitude-encoded pictures up to the measurement, with no trained classical network before or after them, each class's Born probability above its no-information share, $P_g - b_g$, splits exa...

Teerawat Tongloy, R. Jomtarak · 1 citation

#machine learning Preprint Nov 2026

Reading Why: Born-Measured Attention with Exact Word Evidence on Light and Qubits

When a language model answers, we can usually only estimate why. We build a transformer in which every step is a Born measurement - attention $|\langle q|k\rangle|^4$ normalised over each row, a measured product in place of the feed-forward nonlinearity, and a Born readout. With its denominators held at their values fo...

Teerawat Tongloy, Jiraporn Pongsopa, R. Jomtarak · 0 citations