mlsanityData to 7 Oct 2026

Hierarchical Quantum Control Gates for Functional MRI Understanding

arXiv 2408.03596 ↗First posted 7 Aug 2024quant-ph cs.CVcited by 8

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Abstract (arXiv)

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.

Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). Builds on 25 of its 44 references that are in the map. First posted on arXiv 7 Aug 2024.

Builds on

25 of 44 references.

References · 25

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  4. 2018Barren plateaus in quantum neural network training landscapescited by 624
  5. 2019Parameterized quantum circuits as machine learning modelscited by 330
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13 more
  1. 2018Decoding Brain Representations by Multimodal Learning of Neural Activity and Visual Featurescited by 52
  2. 2021Variational Quantum Reinforcement Learning via Evolutionary Optimizationcited by 50
  3. 2023Micron-BERT: BERT-based Facial Micro-Expression Recognitioncited by 43
  4. 2023SwiFT: Swin 4D fMRI Transformercited by 36
  5. 2023Insect-Foundation: A Foundation Model and Large-scale 1M Dataset for Visual Insect Understandingcited by 29
  6. 2022Two-Dimensional Quantum Material Identification via Self-Attention and Soft-labeling in Deep Learningcited by 15
  7. 2023Quantum Vision Clusteringcited by 13
  8. 2023The Algonauts Project 2023 Challenge: UARK-UAlbany Team Solutioncited by 11
  9. 2024QClusformer: A Quantum Transformer-based Framework for Unsupervised Visual Clusteringcited by 11
  10. 2023Fairness in Visual Clustering: A Novel Transformer Clustering Approachcited by 10
  11. 2024Quantum Visual Feature Encoding Revisitedcited by 10
  12. 2024Diffusion-Inspired Quantum Noise Mitigation in Parameterized Quantum Circuitscited by 10
  13. 2024BRACTIVE: A Brain Activation Approach to Human Visual Brain Learningcited by 6

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 2; 2025: 3; 2026: 3.

8 papers.

  1. 2024Quantum-Brain: Quantum-Inspired Neural Network Approach to Vision-Brain Understandingcited by 7
  2. 2025QMoE: A Quantum Mixture of Experts Framework for Scalable Quantum Neural Networkscited by 7
  3. 2024COBRA: A Continual Learning Approach to Vision-Brain Understandingcited by 4
  4. 2025CLIFF: Continual Learning for Incremental Flake Features in 2D Material Identificationcited by 2
  5. 2026OpenQlaw: An Agentic AI Assistant for Analysis of 2D Quantum Materialscited by 1
  6. 2025BRAIN: Bias-Mitigation Continual Learning Approach to Vision-Brain Understandingcited by 0
  7. 2026QLAM: A Quantum Long-Attention Memory Approach to Long-Sequence Token Modelingcited by 0
  8. 2026QuPAINT: Physics-Aware Multimodal Reasoning for Quantum Material Characterizationcited by 0