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量子肿瘤学:量子计算在癌症研究中的应用

Quantum Oncology: The Applications of Quantum Computing in Cancer Research.

作者信息

Li Jiaran, Li Li, Liu Ning, Xu Fuhao, Hu Tao, Yuan Shuanghu

机构信息

Department of Radiation Oncology, Division of Life Sciences and Medicine, The First Affiliated Hospital of USTC, University of Science and Technology of China, No.17 Lujiang Road, Hefei, 230001, Anhui, China.

Department of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University, Shandong Academy of Medical Sciences, Jinan, Shandong, China.

出版信息

J Med Syst. 2025 Jul 23;49(1):99. doi: 10.1007/s10916-025-02215-x.


DOI:10.1007/s10916-025-02215-x
PMID:40699395
Abstract

A global technological race is underway to develop increasingly powerful and precise quantum computers. As a transformative computing paradigm, quantum computing offers the potential for exponentially accelerating specific algorithms, thereby providing the necessary computational power to process vast amounts of data. In light of the challenges classical computing faces with the complexity and volume of oncology data, we introduce the concept of "quantum oncology" and explore its potential applications throughout the cancer care continuum. Additionally, we address several challenges and potential solutions for integrating quantum computing into oncology research. By illuminating these issues, we aim to deepen the understanding of quantum computing's potential in oncology and advocate for multidisciplinary collaboration to propel the advancement of precision oncology. CLINICAL TRIAL NUMBER: Not applicable.

摘要

一场全球性的技术竞赛正在展开,旨在开发功能越来越强大、精度越来越高的量子计算机。作为一种变革性的计算范式,量子计算具有指数级加速特定算法的潜力,从而提供处理海量数据所需的计算能力。鉴于经典计算在肿瘤学数据的复杂性和数量方面面临挑战,我们引入了“量子肿瘤学”的概念,并探讨其在整个癌症护理连续过程中的潜在应用。此外,我们还讨论了将量子计算整合到肿瘤学研究中的若干挑战和潜在解决方案。通过阐明这些问题,我们旨在加深对量子计算在肿瘤学中潜力的理解,并倡导多学科合作以推动精准肿瘤学的发展。临床试验编号:不适用。

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本文引用的文献

[1]
A hybrid quantum computing pipeline for real world drug discovery.

Sci Rep. 2024-7-23

[2]
Breast cancer diagnosis using support vector machine optimized by improved quantum inspired grey wolf optimization.

Sci Rep. 2024-5-10

[3]
Biomarker discovery with quantum neural networks: a case-study in CTLA4-activation pathways.

BMC Bioinformatics. 2024-4-12

[4]
Artificial Intelligence in Oncology: Current Landscape, Challenges, and Future Directions.

Cancer Discov. 2024-5-1

[5]
Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries.

CA Cancer J Clin. 2024

[6]
Big data and artificial intelligence in cancer research.

Trends Cancer. 2024-2

[7]
Quantum gate algorithm for reference-guided DNA sequence alignment.

Comput Biol Chem. 2023-12

[8]
Error mitigation enables PET radiomic cancer characterization on quantum computers.

Eur J Nucl Med Mol Imaging. 2023-11

[9]
Quantum sensors for biomedical applications.

Nat Rev Phys. 2023

[10]
Signatures of Room-Temperature Quantum Interference in Molecular Junctions.

Acc Chem Res. 2023-2-7

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