许多体量子态的随机表示
Hristiana Atanasova1, Liam Bernheimer1, Guy Cohen2,3
1School of Chemistry, Tel Aviv University, Tel Aviv, 6997801, Israel.
Nature communications
|June 16, 2023
概括
这项研究引入了一种机器学习方法来解决量子多体问题. 通过将量子波函数表示为数据点,它通过回归简化了查找基本状态,提高了计算可扩展性.
科学领域:
- 量子力学就是量子力学.
- 计算物理学的计算物理.
- 机器学习是机器学习.
背景情况:
- 量子多体问题面临着维度的诅咒,使得数值解决方案在计算上昂贵.
- 深度神经网络擅长处理高维,相关的功能.
研究的目的:
- 开发一种计算可扩展的方法来解决量子多体问题.
- 利用机器学习进行高效的量子波函数表示和基本状态确定.
主要方法:
- 在抽样点中随机表示波函数.
- 将基本状态的搜索简化为监督学习回归任务.
- 为了数据增强,利用费米离子/玻色离子波函数的 (反) 对称性特性.
主要成果:
- 证明了基本状态搜索可以作为回归问题的框架.
- 通过波函数对称性属性展示了数据增强.
- 实现了更强大的和可扩展的ansatz向地面状态的传播.
结论:
- 机器学习为量子多体问题提供了传统方法的可行和可扩展的替代方案.
- 随机波函数表示简化了复杂的量子计算.
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