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| Autori principali: | , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2508.05063 |
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| _version_ | 1866912525144031232 |
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| author | Rajput, Nikhil Kumar Bansal, Riya |
| author_facet | Rajput, Nikhil Kumar Bansal, Riya |
| contents | Quantum computing holds transformative potential for medical applications, yet efficiently preparing quantum states from complex medical data remains a fundamental challenge. This survey provides a comprehensive examination of current approaches for encoding medical information into quantum systems, analyzing theoretical principles, algorithmic advancements, and practical limitations. It discusses tensor network decomposition, variational quantum algorithms, quantum machine learning techniques, and specialized error mitigation strategies for medical computing. The findings indicate that quantum advantages in medicine rely on leveraging inherent data structures such as spatial correlations in imaging, temporal patterns in physiological signals, and hierarchical biological organization. While current hardware restricts implementations to small-scale problems, emerging methods show potential for near-term use. The study provides a structured framework for assessing when quantum state preparation outperforms classical approaches in medicine, along with implementation guidelines and performance benchmarks. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_05063 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Quantum State Preparation for Medical Data: Comprehensive Methods, Implementation Challenges, and Clinical Prospects Rajput, Nikhil Kumar Bansal, Riya Quantum Physics Quantum computing holds transformative potential for medical applications, yet efficiently preparing quantum states from complex medical data remains a fundamental challenge. This survey provides a comprehensive examination of current approaches for encoding medical information into quantum systems, analyzing theoretical principles, algorithmic advancements, and practical limitations. It discusses tensor network decomposition, variational quantum algorithms, quantum machine learning techniques, and specialized error mitigation strategies for medical computing. The findings indicate that quantum advantages in medicine rely on leveraging inherent data structures such as spatial correlations in imaging, temporal patterns in physiological signals, and hierarchical biological organization. While current hardware restricts implementations to small-scale problems, emerging methods show potential for near-term use. The study provides a structured framework for assessing when quantum state preparation outperforms classical approaches in medicine, along with implementation guidelines and performance benchmarks. |
| title | Quantum State Preparation for Medical Data: Comprehensive Methods, Implementation Challenges, and Clinical Prospects |
| topic | Quantum Physics |
| url | https://arxiv.org/abs/2508.05063 |