Attention-Like Hebbian Learning from Quantum Probability Flow and Quantum-Annealer Tests
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arXiv
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| Format: | Preprint |
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2026
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| _version_ | 1866917555152617472 |
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| author | Ohzeki, Masayuki |
| author_facet | Ohzeki, Masayuki |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2606_02098 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Attention-Like Hebbian Learning from Quantum Probability Flow and Quantum-Annealer Tests Ohzeki, Masayuki Quantum Physics Disordered Systems and Neural Networks 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. |
| title | Attention-Like Hebbian Learning from Quantum Probability Flow and Quantum-Annealer Tests |
| topic | Quantum Physics Disordered Systems and Neural Networks |
| url | https://arxiv.org/abs/2606.02098 |