量子机器学习:对将人工智能与量子计算集成为计算进步的全面审查
Raghavendra M Devadas1, Sowmya T2
1Department of Information Technology, Manipal Institute of Technology Bengaluru, Manipal Academy of Higher Education, Manipal, India.
MethodsX
|May 7, 2025
概括
量子机器学习 (QML) 融合了量子计算和人工智能,以实现强大的问题解决. 这篇评论探讨了QML.
科学领域:
- 量子计算是一种量子计算.
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 量子机器学习 (QML) 将量子计算与人工智能相结合.
- 它利用诸如叠加和纠等量子现象来增强数据处理.
- 对于复杂的计算问题,QML提供了超越经典能力的潜在解决方案.
研究的目的:
- 提供对量子机器学习当前状态的全面概述.
- 探索从量子增强的经典ML到原生量子算法和混合框架的进步.
- 突出QML应用和未来的研究方向.
主要方法:
- 审查QML的理论基础.
- 分析各种行业的实际应用.
- 确定当前的挑战和未来的研究途径.
主要成果:
- 在机器学习任务中,QML展示了指数级加快速度的潜力.
- 应用范围包括优化,药物发现和安全通信.
- 显著的挑战包括杂的量子比特,错误纠正和数据编码限制.
结论:
- 对于医疗保健,金融和物流等行业来说,QML具有变革性的潜力.
- 跨学科的研究对于克服现有的技术障碍至关重要.
- 持续开发对于实现QML的全部承诺至关重要.
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