# Quantum Model Parallelism for MRI-Based Classification of Alzheimer's Disease Stages

arXiv: https://arxiv.org/abs/2602.00128 · HTML: https://mlsanity.com/paper/2602.00128/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 16 of its 94 references that are in the map.
- First posted on arXiv 28 Jan 2026.

## Builds on (most cited first, 16 of 16)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Parameterized quantum circuits as machine learning models](https://mlsanity.com/paper/1906.07682/) (2019)
- [Classification with Quantum Machine Learning: A Survey](https://mlsanity.com/paper/2006.12270/) (2020)
- [CompressedMediQ: Hybrid Quantum Machine Learning Pipeline for High-Dimensional Neuroimaging Data](https://mlsanity.com/paper/2409.08584/) (2024)
- [QClusformer: A Quantum Transformer-based Framework for Unsupervised Visual Clustering](https://mlsanity.com/paper/2405.19722/) (2024)
- [Forecasting the Progression of Alzheimer's Disease Using Neural Networks and a Novel Pre-Processing Algorithm](https://mlsanity.com/paper/1903.07510/) (2019)
- [A Review of Machine Learning Classification Using Quantum Annealing for Real-world Applications](https://mlsanity.com/paper/2106.02964/) (2021)
- [Hybrid quantum-classical graph neural networks for tumor classification in digital pathology](https://mlsanity.com/paper/2310.11353/) (2023)
- [Biomarker Discovery with Quantum Neural Networks: A Case-study in CTLA4-Activation Pathways](https://mlsanity.com/paper/2306.01745/) (2023)
- [Quantum Machine Learning in the Cognitive Domain: Alzheimer's Disease Study](https://mlsanity.com/paper/2401.06697/) (2023)
- [Quantum AI for Alzheimer's disease early screening](https://mlsanity.com/paper/2405.00755/) (2024)
- [Comparing Quantum Encoding Techniques](https://mlsanity.com/paper/2410.09121/) (2024)
- [A Quick Guide to Quantum Communication](https://mlsanity.com/paper/2402.15707/) (2024)
- [Evaluating the Impact of Different Quantum Kernels on the Classification Performance of Support Vector Machine Algorithm: A Medical Dataset Application](https://mlsanity.com/paper/2407.09930/) (2024)
- [Image-Based Alzheimer's Disease Detection Using Pretrained Convolutional Neural Network Models](https://mlsanity.com/paper/2502.05815/) (2025)
- [CQ CNN: A Hybrid Classical Quantum Convolutional Neural Network for Alzheimer's Disease Detection Using Diffusion Generated and U Net Segmented 3D MRI](https://mlsanity.com/paper/2503.02345/) (2025)

## Abstract (arXiv)

With increasing life expectancy, AD has become a major global health concern. While classical AI-based methods have been developed for early diagnosis and stage classification of AD, growing data volumes and limited computational resources necessitate faster, more efficient approaches. Quantum-based AI methods, which leverage superposition and entanglement principles along with high-dimensional Hilbert space, can surpass classical approaches' limitations and offer higher accuracy for high-dimensional, heterogeneous, and noisy data. In this study, a Quantum-Based Parallel Model (QBPM) architecture is proposed for the efficient classification of AD stages using MRI datasets, inspired by the principles of classical model parallelism. The proposed model leverages quantum advantages by employing two distinct quantum circuits, each incorporating rotational and entanglement blocks, running in parallel on the same quantum simulator. The classification performance of the model was evaluated on two different datasets to assess its overall robustness and generalization capability. The proposed model demonstrated high classification accuracy across both datasets, highlighting its overall robustness and generalization capability. Results obtained under high-level Gaussian noise, simulating real-world conditions, further provided experimental evidence for the model's applicability not only in theoretical but also in practical scenarios. Moreover, compared with five different classical transfer learning methods, the proposed model demonstrated its efficiency as an alternative to classical approaches by achieving higher classification accuracy and comparable execution time while utilizing fewer circuit parameters. The results indicate that the proposed QBPM architecture represents an innovative and powerful approach for the classification of stages in complex diseases such as Alzheimer's.