在最大表达性时对量子神经网络的回归
Iván Panadero1,2,3, Yue Ban4,5, Hilario Espinós4
1Departamento de Física, Universidad Carlos III de Madrid, Avda. de la Universidad 30, 28911, Leganés, Spain. ipanadero@gmail.com.
Scientific reports
|December 31, 2024
概括
这项研究使用量子比特探索了通用深度神经网络. 全球纠测量通过和表达界限来增强网络近似能力,与本地测量不同.
科学领域:
- 量子计算是一种量子计算.
- 人工智能的人工智能是人工智能.
- 机器学习 机器学习
背景情况:
- 深度神经网络 (DNN) 是强大的功能估计器.
- 量子神经网络 (QNN) 在计算能力方面提供了潜在的优势.
- 了解QNN表达力对于实际应用至关重要.
研究的目的:
- 分析一个以嵌套量子比特旋转结构的通用深度神经网络的表达性.
- 量化网络在回归任务中近似连续函数的能力.
- 调查测量策略对QNN性能的影响.
主要方法:
- 利用具有可调节数据重新上传和嵌套量子位旋转的通用深度神经网络.
- 通过输出部分里埃分解量化表达力.
- 使用教师-学生方案进行基准绩效测试.
- 将全球纠测量与本地量子比特读数进行比较.
主要成果:
- 网络表达力随深度和量子位数量而变化,但受到数据编码的限制.
- 测量技术显著影响到最大的表达界限的实现.
- 全球纠测量和表达界限,增强近似能力.
- 这种增强与更广泛的富里埃波的生存集合有关.
结论:
- 全球纠测量是释放量子神经网络全部潜力的关键.
- 优化测量策略可以显著提高量子机器学习模型的性能.
- 这些发现为设计更强大,更有效的量子神经网络架构提供了洞察力.
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