Two steps of gradient descent on first-layer weights in linear-width two-layer networks produce a spiked random matrix with floor(alpha2/(1/2-alpha1)) outliers, each a learned direction, and batch reuse allows capturing directions with information exponent exceeding one.
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For any distribution of pure n-qubit states, a QAE with k encoder ancillas and n decoder ancillas achieves the optimal average fidelity among all CPTP encoder-decoder pairs, with the encoder threshold proven sharp.
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Feature Learning in Linear-Width Two-Layer Networks: Two vs. One Step of Gradient Descent
Two steps of gradient descent on first-layer weights in linear-width two-layer networks produce a spiked random matrix with floor(alpha2/(1/2-alpha1)) outliers, each a learned direction, and batch reuse allows capturing directions with information exponent exceeding one.
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Toward the Goldilocks blind compression of quantum states
For any distribution of pure n-qubit states, a QAE with k encoder ancillas and n decoder ancillas achieves the optimal average fidelity among all CPTP encoder-decoder pairs, with the encoder threshold proven sharp.