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| Main Authors: | , , , , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2502.13140 |
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| _version_ | 1866916621297123328 |
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| author | Burch, Myson Zhang, Jiasen Idumah, Gideon Doga, Hakan Lartey, Richard Yehia, Lamis Yang, Mingrui Yildirim, Murat Karaayvaz, Mihriban Shehab, Omar Guo, Weihong Ni, Ying Parida, Laxmi Li, Xiaojuan Bose, Aritra |
| author_facet | Burch, Myson Zhang, Jiasen Idumah, Gideon Doga, Hakan Lartey, Richard Yehia, Lamis Yang, Mingrui Yildirim, Murat Karaayvaz, Mihriban Shehab, Omar Guo, Weihong Ni, Ying Parida, Laxmi Li, Xiaojuan Bose, Aritra |
| contents | Tensor decomposition has emerged as a powerful framework for feature extraction in multi-modal biomedical data. In this review, we present a comprehensive analysis of tensor decomposition methods such as Tucker, CANDECOMP/PARAFAC, spiked tensor decomposition, etc. and their diverse applications across biomedical domains such as imaging, multi-omics, and spatial transcriptomics. To systematically investigate the literature, we applied a topic modeling-based approach that identifies and groups distinct thematic sub-areas in biomedicine where tensor decomposition has been used, thereby revealing key trends and research directions. We evaluated challenges related to the scalability of latent spaces along with obtaining the optimal rank of the tensor, which often hinder the extraction of meaningful features from increasingly large and complex datasets. Additionally, we discuss recent advances in quantum algorithms for tensor decomposition, exploring how quantum computing can be leveraged to address these challenges. Our study includes a preliminary resource estimation analysis for quantum computing platforms and examines the feasibility of implementing quantum-enhanced tensor decomposition methods on near-term quantum devices. Collectively, this review not only synthesizes current applications and challenges of tensor decomposition in biomedical analyses but also outlines promising quantum computing strategies to enhance its impact on deriving actionable insights from complex biomedical data. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_13140 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Towards Quantum Tensor Decomposition in Biomedical Applications Burch, Myson Zhang, Jiasen Idumah, Gideon Doga, Hakan Lartey, Richard Yehia, Lamis Yang, Mingrui Yildirim, Murat Karaayvaz, Mihriban Shehab, Omar Guo, Weihong Ni, Ying Parida, Laxmi Li, Xiaojuan Bose, Aritra Quantitative Methods Machine Learning Tensor decomposition has emerged as a powerful framework for feature extraction in multi-modal biomedical data. In this review, we present a comprehensive analysis of tensor decomposition methods such as Tucker, CANDECOMP/PARAFAC, spiked tensor decomposition, etc. and their diverse applications across biomedical domains such as imaging, multi-omics, and spatial transcriptomics. To systematically investigate the literature, we applied a topic modeling-based approach that identifies and groups distinct thematic sub-areas in biomedicine where tensor decomposition has been used, thereby revealing key trends and research directions. We evaluated challenges related to the scalability of latent spaces along with obtaining the optimal rank of the tensor, which often hinder the extraction of meaningful features from increasingly large and complex datasets. Additionally, we discuss recent advances in quantum algorithms for tensor decomposition, exploring how quantum computing can be leveraged to address these challenges. Our study includes a preliminary resource estimation analysis for quantum computing platforms and examines the feasibility of implementing quantum-enhanced tensor decomposition methods on near-term quantum devices. Collectively, this review not only synthesizes current applications and challenges of tensor decomposition in biomedical analyses but also outlines promising quantum computing strategies to enhance its impact on deriving actionable insights from complex biomedical data. |
| title | Towards Quantum Tensor Decomposition in Biomedical Applications |
| topic | Quantitative Methods Machine Learning |
| url | https://arxiv.org/abs/2502.13140 |