| Title of paper:
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How to represent uncertainty via qudits: Probability distributions, regular, intuitionistic and picture fuzzy sets, F-transforms, etc.
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| Author(s):
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| Olga Kosheleva
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| Department of Teacher Education, University of Texas at El Paso, 500 W. University, El Paso, Texas 79968, USA
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| olgak@utep.edu
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| Vladik Kreinovich
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| Department of Teacher Education, University of Texas at El Paso, 500 W. University, El Paso, Texas 79968, USA
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| vladik@utep.edu
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| Presented at:
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25th ICIFS, Sofia, 9—10 September 2022
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| Published in:
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Notes on Intuitionistic Fuzzy Sets, Volume 28 (2022), Number 3, pages 203–210
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| DOI:
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https://doi.org/10.7546/nifs.2022.28.3.203-210
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| Download:
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 PDF (153 Kb, File info)
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| Abstract:
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While modern computers are fast, there are still many important practical situations in which we need even faster computations. It turns out that, due to the fact that the speed of all communications is limited by the speed of light, the only way to make computers drastically faster is to drastically decrease the size of computer’s components. When we decrease their size to sizes comparable with micro-sizes of individual molecules, it becomes necessary to take into account specific physics of the micro-world – known as quantum physics. Traditional approach to designing quantum computers – i.e., computers that take effect of quantum physics into account – was based on using quantum analogies of bits (2-state systems). However, it has recently been shown that the use of multi-state quantum systems – called qudits – can make quantum computers even more efficient.
When processing data, it is important to take into account that in practice, data always comes with uncertainty. In this paper, we analyze how to represent different types of uncertainty by qudits.
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| Keywords:
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Quantum computing, Qudits, Uncertainty, Fuzzy, Intuitionistic fuzzy, Picture fuzzy, Probabilistic uncertainty, F-transform.
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| AMS Classification:
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68Q12, 81P68, 03B52, 03E72, 68T27, 68T37.
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