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Open Quantum Systems Decoherence Quantum Machine Learning

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

arXiv

Authors: Masayuki Ohzeki

Year

2026

Paper ID

67986

Status

Preprint

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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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This paper contributes to the Quantum Machine Learning research area in the Quantum Articles archive.
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We propose a quantum probability-flow principle for deriving local learning rules in associative memory.

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References & Citation Signals

[1] DOI https://doi.org/arXiv:2606.02098 [2] arXiv https://arxiv.org/abs/2606.02098 [3] Publisher https://arxiv.org/abs/2606.02098

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External citation index: OpenAlex citation signal • updated 2026-06-19 12:51:57

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