Modeling stochastic eye tracking data: A comparison of quantum generative adversarial networks and Markov models

TL;DR

This paper compares quantum generative adversarial networks and Markov models for modeling eye movement velocity data, finding that Markov models currently outperform QGANs in accurately replicating real data distributions.

Contribution

It provides a comparative analysis of QGANs and Markov models for stochastic eye tracking data, highlighting current limitations and future directions for quantum-based modeling.

Findings

Markov models outperform QGANs in data accuracy

QGANs show potential but need further optimization

Study advances understanding of quantum models in time series

Abstract

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.