在量子实验数据上进行机器学习,以解决量子多体问题
1Department of Physics and Astronomy, and Institute of Applied Physics, Seoul National University, Seoul, 08826, South Korea. km950501@snu.ac.kr.
Nature communications
|August 30, 2024
概括
我们将经典机器学习 (ML) 与量子计算数据相结合,以解决多体物理学中的复杂问题. 这种混合方法成功处理了高达44量子比特的系统的量子实验数据.
科学领域:
- 量子计算是一种量子计算.
- 机器学习 机器学习
- 多体物理多体物理
背景情况:
- 量子硬件产生了无法用于经典模拟的数据.
- 经典机器学习 (ML) 与量子数据的整合提供了模式发现潜力.
- 目前的噪音量子计算机限制了混合方法的应用.
研究的目的:
- 将混合量子-经典ML扩展到多体物理问题.
- 预测基本状态属性,并对量子相进行分类.
- 在精细的量子实验数据上证明ML算法的有效性.
主要方法:
- 使用了127个量子位的超导量子硬件.
- 应用了减少错误的程序,以获取精细的量子数据.
- 实施了用于量子系统分析的经典ML算法.
主要成果:
- 成功地将经典的ML算法应用于量子实验数据.
- 对于高达44个量子比特的系统,已经证明了可行性.
- 验证了ML用于量子数据处理的可扩展性和有效性.
结论:
- 混合量子-经典ML方法对多体物理学是有效的.
- 精制的量子数据可以实现先进的ML应用.
- 这种方法对分析复杂的量子系统具有前景.
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