为学习量子动力学而进行分布外概括
Matthias C Caro1,2,3,4, Hsin-Yuan Huang5,6, Nicholas Ezzell7,8
1Department of Mathematics, Technical University of Munich, Garching, Germany. matthias.caro@fu-berlin.de.
Nature communications
|July 5, 2023
概括
量子机器学习 (QML) 模型现在可以在训练数据分布之外进行概括. 这项研究证明了学习未知的单元数的分布外概括,即使是在简单的产品状态上训练时也是如此.
科学领域:
- 量子机器学习的方法
- 量子计算理论 量子计算理论
- 一般化界限 一般化界限
背景情况:
- 一般化界限对于理解量子机器学习 (QML) 中的数据需求至关重要.
- 现有的QML研究保证了量子神经网络 (QNN) 的分布式泛化.
- 在QML中,分布外 (OOD) 泛化仍然是一个公开的挑战,限制了模型适用于未见的数据分布.
研究的目的:
- 在量子机器学习中建立分布外概括的理论保证.
- 证明从不同分布生成的数据中学习未知的量子单元的能力.
- 探索对近期量子硬件和量子电路编译的影响.
主要方法:
- 量子模型的概括界限的理论分析.
- 证明在学习一个未知的单元的特定任务的分布外概括.
- 使用产品状态进行训练和评估纠状态的概括.
主要成果:
- 这项研究证明了在QML中学习未知单元的分布外概括.
- 证明了在产物状态上训练的量子模型可以学习单元对纠状态的作用.
- 这种理论上的进步是在不需要从目标分布中获得训练数据的情况下实现的.
结论:
- 这项工作弥合了对QML的OOD概括理解的差距.
- 这些发现表明,在近期的量子硬件上使用更简单的训练状态来学习量子动力学是可行的.
- 结果为古典和量子电路编译策略提供了新的途径.
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