通过使用经典神经网络的变量量子电路来估计纠
Sangyun Lee1,2, Hyukjoon Kwon3, Jae Sung Lee2
1Institute for Physical Science and Technology, University of Maryland, College Park, Maryland 20742, USA.
Physical review. E
|May 17, 2024
概括
我们开发了一个量子神经估计器 (QNEE),它使用神经网络和量子电路来准确估计量子状态. 此外,QNEE还对量子相进行了分类,并确定了关键点,帮助了量子信息科学.
科学领域:
- 量子信息科学 量子信息科学
- 计算物理 计算物理
- 机器学习 机器学习
背景情况:
- 是古典和量子物理学的基础,对信息科学至关重要.
- 估计量子和分类量子相是具有挑战性但至关重要的任务.
研究的目的:
- 引入量子神经值估计器 (QNEE),一种混合的经典-量子方法.
- 为了准确估计量子状态的·诺曼和雷尼 entropies.
- 为了分类量子相,并使用纠变的方法来识别相变.
主要方法:
- 将经典的神经网络 (NN) 与变量量子电路相结合.
- 使用QNEE来估计量子状态,固有值和固有状态.
- 将QNEE应用于数字模拟的1D XXZ海森堡模型.
主要成果:
- QNEE准确地估计了量子,并提供了自身值/自身状态.
- 该方法成功地根据纠变化对量子相进行了分类.
- QNEE在检测相位过渡附近的纠变量方面表现出高灵敏度.
结论:
- QNEE是用于量子估计和相位分类的有效工具.
- 混合方法为分析复杂量子系统提供了一种强大的方法.
- 在量子信息科学和凝聚物质物理学方面,QNEE显示出前景.
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