Nonlinear Maccone-Pati Uncertainty Principle
Fuente:
arXiv
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866916123577942016 |
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| author | Krishna, K. Mahesh |
| author_facet | Krishna, K. Mahesh |
| contents | We show that one of the two important uncertainty principles derived by Maccone and Pati \textit{[Phys. Rev. Lett., 2014]} can be derived for arbitrary maps defined on subsets of $\mathcal{L}^p$ spaces for $1< p<\infty$. Our main tool is the Clarkson inequalities. We also derive a nonlinear uncertainty principle for weak parallelogram spaces and Type-p Banach spaces. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_08591 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Nonlinear Maccone-Pati Uncertainty Principle Krishna, K. Mahesh Functional Analysis Information Theory Mathematical Physics 46B20, 46E30 We show that one of the two important uncertainty principles derived by Maccone and Pati \textit{[Phys. Rev. Lett., 2014]} can be derived for arbitrary maps defined on subsets of $\mathcal{L}^p$ spaces for $1< p<\infty$. Our main tool is the Clarkson inequalities. We also derive a nonlinear uncertainty principle for weak parallelogram spaces and Type-p Banach spaces. |
| title | Nonlinear Maccone-Pati Uncertainty Principle |
| topic | Functional Analysis Information Theory Mathematical Physics 46B20, 46E30 |
| url | https://arxiv.org/abs/2402.08591 |