带有生成神经网络的自旋链的雷尼纠
Piotr Białas1, Piotr Korcyl2, Tomasz Stebel2
1Institute of Applied Computer Science, <a href="https://ror.org/03bqmcz70">Jagiellonian University</a>, ul. Łojasiewicza 11, 30-348 Kraków, Poland.
Physical review. E
|November 20, 2024
概括
我们开发了一种使用生成神经网络来估计量子自旋系统中的雷尼纠的新方法. 这种方法准确地模拟了自旋链,可以应用于各种量子理论.
科学领域:
- 量子信息理论 量子信息理论
- 计算物理 计算物理
- 机器学习 机器学习
背景情况:
- 估计纠是了解量子多体系统的关键.
- 对于较大的系统,传统方法可能是计算密集型的.
研究的目的:
- 引入一种新的,有效的方法来计算雷尼纠.
- 为了证明该方法对量子自旋系统和格子场理论的适用性.
主要方法:
- 使用复制技巧与生成神经网络相结合.
- 使用自回归网络的层次结构来进行明确的概率估计.
- 模拟一个一维的量子Ising自旋链,可达32个自旋.
主要成果:
- 成功计算了第二个雷尼和其衍生值的自旋链.
- 根据既定的数值方法和文献数据验证了结果.
- 证明了生成神经网络方法的可扩展性和准确性.
结论:
- 拟议的方法提供了一个高效和多功能工具,用于纠的估计.
- 生成性神经网络为模拟复杂的量子系统提供了一个强大的框架.
- 这种技术对量子物理学和机器学习应用有着广泛的影响.
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