Attention-Like Hebbian Learning from Quantum Probability Flow and Quantum-Annealer Tests

Masayuki Ohzeki

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arXiv · 2606.02098

Attention-Like Hebbian Learning from Quantum Probability Flow and Quantum-Annealer Tests

Masayuki Ohzeki

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Abstract

We propose a quantum probability-flow principle for deriving local learning rules in associative memory. A transverse field defines leakage channels from data states, and minimizing the measured survival loss gives stability-driven updates. For imaginary-time, dephased dynamics, the local leakage free energy is the log-sum-exp of energy gaps; its gradient is a softmax-weighted Hebbian rule. Real-time stability instead yields a power-law weighting. D-Wave standard- and fast-anneal tests of a one-hot attention forward map are better fitted by an effective softmax than by a Lorentzian power law.

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Masayuki Ohzeki. 2026-06-01. Attention-Like Hebbian Learning from Quantum Probability Flow and Quantum-Annealer Tests. https://arxiv.org/abs/2606.02098

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