The Universe as a Learning System
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arXiv
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866910343053180928 |
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| author | Shushi, Tomer |
| author_facet | Shushi, Tomer |
| contents | At its microscopic level, the universe follows the laws of quantum mechanics. Focusing on the quantum trajectories of particles as followed from the hydrodynamical formulation of quantum mechanics, we propose that under general requirements, quantum systems follow a disrupted version of the gradient descent model, a basic machine learning algorithm, where the learning is distorted due to the self-organizing process of the quantum system. Such a learning process is possible only when we assume dissipation, i.e., that the quantum system is open. The friction parameter determines the nonlinearity of the quantum system. We then provide an empirical demonstration of the proposed model. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2402_14423 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | The Universe as a Learning System Shushi, Tomer Quantum Physics Data Analysis, Statistics and Probability Machine Learning At its microscopic level, the universe follows the laws of quantum mechanics. Focusing on the quantum trajectories of particles as followed from the hydrodynamical formulation of quantum mechanics, we propose that under general requirements, quantum systems follow a disrupted version of the gradient descent model, a basic machine learning algorithm, where the learning is distorted due to the self-organizing process of the quantum system. Such a learning process is possible only when we assume dissipation, i.e., that the quantum system is open. The friction parameter determines the nonlinearity of the quantum system. We then provide an empirical demonstration of the proposed model. |
| title | The Universe as a Learning System |
| topic | Quantum Physics Data Analysis, Statistics and Probability Machine Learning |
| url | https://arxiv.org/abs/2402.14423 |