  Quantum computing has emerged as a powerful tool for solving complex problems
intractable for classical computers, particularly in popular fields such as
cryptography, optimization, and neurocomputing. In this paper, we present a new
quantum-based approach named the Hierarchical Quantum Control Gates (HQCG)
method for efficient understanding of Functional Magnetic Resonance Imaging
(fMRI) data. This approach includes two novel modules: the Local Quantum
Control Gate (LQCG) and the Global Quantum Control Gate (GQCG), which are
designed to extract local and global features of fMRI signals, respectively.
Our method operates end-to-end on a quantum machine, leveraging quantum
mechanics to learn patterns within extremely high-dimensional fMRI signals,
such as 30,000 samples which is a challenge for classical computers. Empirical
results demonstrate that our approach significantly outperforms classical
methods. Additionally, we found that the proposed quantum model is more stable
and less prone to overfitting than the classical methods.
