Modeling stochastic eye tracking data: A comparison of quantum generative adversarial networks and Markov models
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arXiv
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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| _version_ | 1866909417760358400 |
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| author | Bhandari, Shailendra Lincastre, Pedro Lind, Pedro |
| author_facet | Bhandari, Shailendra Lincastre, Pedro Lind, Pedro |
| contents | We explore the use of quantum generative adversarial networks QGANs for modeling eye movement velocity data. We assess whether the advanced computational capabilities of QGANs can enhance the modeling of complex stochastic distribution beyond the traditional mathematical models, particularly the Markov model. The findings indicate that while QGANs demonstrate potential in approximating complex distributions, the Markov model consistently outperforms in accurately replicating the real data distribution. This comparison underlines the challenges and avenues for refinement in time series data generation using quantum computing techniques. It emphasizes the need for further optimization of quantum models to better align with real-world data characteristics. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_00673 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Modeling stochastic eye tracking data: A comparison of quantum generative adversarial networks and Markov models Bhandari, Shailendra Lincastre, Pedro Lind, Pedro Neural and Evolutionary Computing Machine Learning Quantum Physics We explore the use of quantum generative adversarial networks QGANs for modeling eye movement velocity data. We assess whether the advanced computational capabilities of QGANs can enhance the modeling of complex stochastic distribution beyond the traditional mathematical models, particularly the Markov model. The findings indicate that while QGANs demonstrate potential in approximating complex distributions, the Markov model consistently outperforms in accurately replicating the real data distribution. This comparison underlines the challenges and avenues for refinement in time series data generation using quantum computing techniques. It emphasizes the need for further optimization of quantum models to better align with real-world data characteristics. |
| title | Modeling stochastic eye tracking data: A comparison of quantum generative adversarial networks and Markov models |
| topic | Neural and Evolutionary Computing Machine Learning Quantum Physics |
| url | https://arxiv.org/abs/2408.00673 |