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.

翻译：我们探讨了使用量子生成对抗网络（QGANs）对眼动速度数据进行建模的方法。我们评估了QGANs先进的计算能力是否能够超越传统数学模型（尤其是马尔可夫模型）来增强对复杂随机分布的建模能力。研究结果表明，尽管QGANs在近似复杂分布方面展现出潜力，但马尔可夫模型在准确复现真实数据分布方面始终表现更优。这一对比凸显了使用量子计算技术生成时间序列数据所面临的挑战与改进方向，并强调需要进一步优化量子模型以更好地契合现实世界的数据特征。