[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124424-en":3,"doc-seo-124424-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},124424,1374391974585,"Genevieve","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","CompressedMediQ - Hybrid Quantum Machine Learning Pipeline for High-Dimensional Neuroimaging Data","This paper introduces CompressedMediQ, a hybrid quantum-classical machine learning pipeline designed to tackle computational bottlenecks in high-dimensional multi-class neuroimaging analysis. Large MRI datasets such as ADNI and NIFD create major obstacles due to scale and complexity, while NISQ-era limited qubit availability constrains direct quantum encoding. The method combines HPC-based MRI pre-processing and CNN-PCA feature extraction, then applies quantum kernel estimation with a QSVM for multi-class dementia-stage classification. Experimental results demonstrate improved accuracy and support quantum-enhanced clinical diagnostics despite NISQ limitations.","arXiv :2409 .08584v3 [ quant-ph] 21 Sep 2024  \nCompressedMediQ:  \nHybrid Quantum Machine Learning Pipeline for High-Dimensional Neuroimaging Data  \nKuan-Cheng Chen†‡∗ , Yi-Tien Li §¶ , Tai-Yu Li ∥ , Chen-Yu Liu∗∗††, Po-Heng (Henry) Lee ‡ Cheng-Yu Chen§‡‡xxi  \n†Department of Electrical and Electronic Engineering, Imperial College London, London, UK ‡Centre for Quantum Engineering, Science and Technology (QuEST), Imperial College London, London, UK  \n§ Translational Imaging Research Center, Taipei Medical University Hospital, Taipei, Taiwan ¶ Research Center for Neuroscience, Taipei Medical University, Taipei, Taiwan  \n¶ Ph.D. Program in Medical Neuroscience, College of Medical Science and Technology, Taipei Medical University, Taipei, Taiwan  \n∥ National Synchrotron Radiation Research Center, Hsinchu, Taiwan ∗∗ Graduate Institute of Applied Physics, National Taiwan University, Taipei, Taiwan ††Hon Hai Research Institute, Taipei, Taiwan  \n‡‡Department of Radiology, School of Medicine, College of Medicine, Taipei, Taiwan  \nx Depxiartment of Medical Imaging, Taipei Department of Radiology, National  \nAbstract—This paper introduces CompressedMediQ, a novel hybrid quantum-classical machine learning pipeline specifically developed to address the computational challenges associated with high-dimensional multi-class neuroimaging data analysis. Standard neuroimaging datasets, such as large-scale MRI data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) and Neuroimaging in Frontotemporal Dementia (NIFD), present significant hurdles due to their vast size and complexity. CompressedMediQ integrates classical high-performance computing (HPC) nodes for advanced MRI pre-processing and Convolutional Neural Network (CNN)-PCA-based feature extraction and reduction, addressing the limited-qubit availability for quantum data encoding in the NISQ (Noisy Intermediate-Scale Quantum) era. This is followed by Quantum Support Vector Machine (QSVM) classification. By utilizing quantum kernel methods, the pipeline optimizes feature mapping and classification, enhancing data separability and outperforming traditional neuroimaging analysis techniques. Experimental results highlight the pipeline’s superior accuracy in dementia staging, validating the practical use of quantum machine learning in clinical diagnostics. Despite the limitations of NISQ devices, this proof-of-concept demonstrates the transformative potential of quantum-enhanced learning, paving the way for scalable and precise diagnostic tools in healthcare and signal processing.  \nIndex Terms—Quantum Machine Learning, Quantum Neural Networks, MRI, Neuroimaging Data  \nI. INTRODUCTION  \nMagnetic Resonance Imaging (MRI) is a cornerstone of neuroscience, providing critical insights into brain structure and function across a variety of neurological conditions, including Alzheimer’s disease (AD) [1], [2] . Traditional ap-  \n*Corresponding Author: [kuan-cheng.chen17@imperial.ac.uk](kuan-cheng.chen17@imperial.ac.uk)  \nMedical University Hospital, Taipei, Taiwan Defense Medical Center, Taipei, Taiwan  \nFig. 1. Overview of the CompressedMediQ Pipeline for High-Dimensional Neuroimaging Data Analysis. The pipeline integrates classical and quantum computing nodes to process MRI data for disease classification. The workflow begins with data pre-processing on HPC classical nodes, including segmentation and voxel-based morphometry analysis, followed by feature extraction using CNNs and dimensionality reduction via PCA. The extracted features are then input into the quantum machine learning stage, where quantum kernel estimation and a QSVM are employed to perform multi-class classification of disease stages.  \nproaches to MRI analysis have predominantly relied on handcrafted feature extraction methods combined with classifiers such as Support Vector Machines (SVM) [3] . Although these methods offer some success, they often struggle with the highdimensional and limited sample size nature of MRI data, le","cbCaiq4pDWz4bWBa","https://ap.wps.com/l/cbCaiq4pDWz4bWBa","pdf",3597578,1,5,"English","en",105,"# Introduction\n## Motivation and challenges in high-dimensional MRI\n# Overview of the CompressedMediQ pipeline\n## Classical HPC pre-processing (segmentation, morphometry)\n## CNN-PCA feature extraction and reduction\n## Quantum stage: quantum kernels and QSVM classification","[{\"question\":\"What problem does CompressedMediQ address in neuroimaging analysis?\",\"answer\":\"It targets the computational challenges of high-dimensional multi-class MRI neuroimaging, especially the difficulty of handling very large data with limited qubit availability in the NISQ era.\"},{\"question\":\"How does CompressedMediQ combine classical and quantum components?\",\"answer\":\"It uses classical HPC for MRI pre-processing and CNN-PCA feature reduction, then feeds the reduced features into a quantum kernel estimation step followed by a QSVM for classification.\"},{\"question\":\"What results does the paper report for dementia staging?\",\"answer\":\"The experimental results indicate superior accuracy for dementia staging, supporting the practical use of quantum machine learning in clinical diagnostics.\"}]","CompressedMediQ - Hybrid Quantum Machine Learning Pipeline for High-Dimensional Neuroimaging Data | PDF",1785822235,13,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"compressedmediq-hybrid-quantum-machine-learning-pipeline-for-high-dimensional-neuroimaging-data","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/compressedmediq-hybrid-quantum-machine-learning-pipeline-for-high-dimensional-neuroimaging-data/124424/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-04",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What problem does CompressedMediQ address in neuroimaging analysis?","Question",{"text":75,"@type":76},"It targets the computational challenges of high-dimensional multi-class MRI neuroimaging, especially the difficulty of handling very large data with limited qubit availability in the NISQ era.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does CompressedMediQ combine classical and quantum components?",{"text":80,"@type":76},"It uses classical HPC for MRI pre-processing and CNN-PCA feature reduction, then feeds the reduced features into a quantum kernel estimation step followed by a QSVM for classification.",{"name":82,"@type":73,"acceptedAnswer":83},"What results does the paper report for dementia staging?",{"text":84,"@type":76},"The experimental results indicate superior accuracy for dementia staging, supporting the practical use of quantum machine learning in clinical diagnostics.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,109,114,119,122,127,130,134],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":106,"show_sort_weight":107,"slug":108},"Comic",60,"comic",{"id":110,"doc_module":4,"doc_module_name":46,"category_name":111,"show_sort_weight":112,"slug":113},6,"Technology",50,"technology",{"id":115,"doc_module":4,"doc_module_name":46,"category_name":116,"show_sort_weight":117,"slug":118},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":120,"slug":121},30,"research-report",{"id":123,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},9,"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":21,"slug":137},19,"General","general"]