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用量子卷积神经网络对物质量子相的模型独立学习
Yu-Jie Liu1,2, Adam Smith3,4, Michael Knap1,2
1Technical University of Munich, TUM School of Natural Sciences, Physics Department, 85748 Garching, Germany.
Physical review letters
|June 16, 2023
概括
研究人员开发了一种新方法来训练量子卷积神经网络 (QCNNs),用于对物质的量子相进行分类. 这种方法成功地使用顺序参数识别了不同的阶段及其边界.
科学领域:
- 量子物理学 量子物理学 是一种量子物理学.
- 机器学习 机器学习
- 凝聚物质理论 凝聚物质理论
背景情况:
- 量子卷积神经网络 (QCNNs) 是用于分类量子相的新兴工具.
- 确定顺序参数对于理解和区分这些阶段至关重要.
研究的目的:
- 提出一个独立于模型的协议,用于训练QCNN发现订单参数.
- 为了使QCNN能够对物质的间隙量子相进行强有力的分类.
主要方法:
- 训练QCNN使用量子相的固点波函数.
- 引入翻译不变噪声以掩盖短距离结构,同时保持对称性.
- 将协议应用于由时间逆向对称保护的单维相位.
主要成果:
- 在QCNN成功地发现了顺序参数,区分微不足道,对称性破坏和对称性保护的拓秩序.
- 训练有素的QCNN准确地预测了相位边界的位置.
- 该协议在各种时间逆转对称模型上表现出有效性.
结论:
- 拟议的协议使量子相分类器的硬件高效培训成为可能.
- 这种方法为发现复杂量子系统中的顺序参数提供了一条途径.
- 这种方法对可编程量子处理器的实际实施充满希望.
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