On the Algorithmic Information Between Probabilities
Fuente:
arXiv
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| Autore principale: | |
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| Natura: | Preprint |
| Pubblicazione: |
2023
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| _version_ | 1866910599461470208 |
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| author | Epstein, Samuel |
| author_facet | Epstein, Samuel |
| contents | We extend algorithmic conservation inequalities to probability measures. The amount of self information of a probability measure cannot increase when submitted to randomized processing. This includes (potentially non-computable) measures over finite sequences, infinite sequences, and $T_0$, second countable topologies. One example is the convolution of signals over real numbers with probability kernels. Thus the smoothing of any signal due We show that given a quantum measurement, for an overwhelming majority of pure states, no meaningful information is produced. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2303_07296 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | On the Algorithmic Information Between Probabilities Epstein, Samuel Computational Complexity Quantum Physics We extend algorithmic conservation inequalities to probability measures. The amount of self information of a probability measure cannot increase when submitted to randomized processing. This includes (potentially non-computable) measures over finite sequences, infinite sequences, and $T_0$, second countable topologies. One example is the convolution of signals over real numbers with probability kernels. Thus the smoothing of any signal due We show that given a quantum measurement, for an overwhelming majority of pure states, no meaningful information is produced. |
| title | On the Algorithmic Information Between Probabilities |
| topic | Computational Complexity Quantum Physics |
| url | https://arxiv.org/abs/2303.07296 |