在横场量子Ising模型中学习多个不连接的间隔的Renyi,使用受限制的博尔兹曼机器
Han-Qing Shi1,2, Hai-Qing Zhang2,3
1Beijing University of Technology, School of Physics and Optoelectronic Engineering, Beijing 100124, China.
Physical review. E
|December 23, 2025
概括
机器学习准确地计算量子系统中的Renyi. 这种使用神经网络的方法与分析复杂磁性质的传统技术相匹配.
科学领域:
- 量子信息理论就是量子信息理论.
- 凝聚物质物理学 凝聚物质物理学
- 计算物理学的计算物理.
背景情况:
- 伦尼量化了量子系统中的纠.
- 计算多个不连接的间隔的Renyi是计算上具有挑战性的.
- 改进的交换操作为纠测量提供了一种新的方法.
研究的目的:
- 使用改进的交换运算计算多个不连接的间隔的Renyi.
- 为了验证机器学习方法对直接对角化对Renyi值计算的验证.
- 在横向场的伊辛格模型中研究Renyi的行为.
主要方法:
- 哈密尔顿式的直接对角化.
- 应用一种利用神经网络的最先进的机器学习方法.
- 实施改进的交换操作,用于纠测量.
主要成果:
- 机器学习方法准确地复制了对Renyi的直接对角化得到的结果.
- 该研究成功计算了二,三和四个不连接的间隔的第二个Renyi.
- 在阶段过渡的关键点上,Renyi度呈现了峰值,然后下降.
结论:
- 机器学习方法是一种可靠和准确的工具,用于计算多个不连接的间隔的Renyi.
- 这些发现证实了基于神经网络的量子信息方法的适用性和高精度.
- 这项研究验证了横场Ising模型中Renyi的理论预测.
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