Bayes or Heisenberg: Who(se) Rules?
Fuente:
arXiv
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| Hauptverfasser: | , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| _version_ | 1866918166418948096 |
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| author | Tresp, Volker Li, Hang Harjes, Federico Ma, Yunpu |
| author_facet | Tresp, Volker Li, Hang Harjes, Federico Ma, Yunpu |
| contents | Although quantum systems are generally described by quantum state vectors, we show that in certain cases their measurement processes can be reformulated as probabilistic equations expressed in terms of probabilistic state vectors. These probabilistic representations can, in turn, be approximated by the neural network dynamics of the Tensor Brain (TB) model.
The Tensor Brain is a recently proposed framework for modeling perception and memory in the brain, providing a biologically inspired mechanism for efficiently integrating generated symbolic representations into reasoning processes. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_13894 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Bayes or Heisenberg: Who(se) Rules? Tresp, Volker Li, Hang Harjes, Federico Ma, Yunpu Neurons and Cognition Artificial Intelligence Machine Learning Quantum Physics Although quantum systems are generally described by quantum state vectors, we show that in certain cases their measurement processes can be reformulated as probabilistic equations expressed in terms of probabilistic state vectors. These probabilistic representations can, in turn, be approximated by the neural network dynamics of the Tensor Brain (TB) model. The Tensor Brain is a recently proposed framework for modeling perception and memory in the brain, providing a biologically inspired mechanism for efficiently integrating generated symbolic representations into reasoning processes. |
| title | Bayes or Heisenberg: Who(se) Rules? |
| topic | Neurons and Cognition Artificial Intelligence Machine Learning Quantum Physics |
| url | https://arxiv.org/abs/2510.13894 |