机器学习辅助构建一个浅深的动态替代器,用于噪音较大的量子硬件
Sonaldeep Halder1, Anish Dey2, Chinmay Shrikhande1
1Department of Chemistry, Indian Institute of Technology Bombay Powai Mumbai 400076 India rmaitra@chem.iitb.ac.in.
Chemical science
|March 1, 2024
概括
这项研究引入了一个新的量子算法,用于在杂的量子计算机上进行分子模拟. 它降低了测量成本,并提高了近期量子计算应用的准确性.
科学领域:
- 量子计算是一种量子计算.
- 计算化学计算化学
- 机器学习 机器学习
背景情况:
- 噪音中等尺度量子 (NISQ) 硬件通过动态替代结构实现分子模拟.
- 当前的替代建筑方法需要高的测量成本,限制了实际应用.
- 开发资源高效的量子算法对于近期的量子计算至关重要.
研究的目的:
- 开发一种用于构建表达性和浅层量子模拟系统的新方案.
- 为了降低测量成本并提高对变量量子自溶解器 (VQE) 算法的硬件噪声的稳定性.
- 为了在NISQ设备上方便准确的分子性质确定.
主要方法:
- 利用再生机器学习和多体扰动理论来识别主导激发的决定因素.
- 在N电子希尔伯特空间的低级扩展上训练机器学习模型.
- 通过低级分解将选定的激发决定因素纳入替代物.
主要成果:
- 实现了量子测量成本和替代深度的显著降低.
- 通过数值模拟证明了对硬件噪声的稳定性.
- 拟议的方法与神经错误缓解技术兼容.
结论:
- 开发的资源效率高的方法对于NISQ硬件上的分子模拟至关重要.
- 能够准确地确定光谱和分子性质.
- 使用近期量子计算机,方便研究新的化学现象.
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