机器学习辅助的维度缩小,实现资源高效的投影量子自解决器:正式开发和试点应用程序
Sonaldeep Halder1, Chayan Patra1, Dibyendu Mondal1
1Department of Chemistry, Indian Institute of Technology Bombay, Powai, Mumbai 400076, India.
The Journal of chemical physics
|June 22, 2023
概括
本研究引入了一种机器学习方法,以减少混合量子-经典算法的量子测量. 这加速了在杂的量子设备上计算分子基本状态能量的计算.
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:
- 混合量子-经典算法对于在杂中等量级量子 (NISQ) 设备上的分子模拟至关重要.
- 目前的方法需要广泛的量子测量来优化参数,导致长时间运行.
- 减少量子硬件依赖对于实际应用至关重要.
研究的目的:
- 开发一种方法,在混合量子-经典算法中大大降低量子测量要求.
- 提高投射量子自溶器 (PQE) 计算基态能量的效率.
- 为NISQ设备创建一种抗噪方法.
主要方法:
- 这是一种跨学科的方法,结合了量子计算和监督机器学习.
- 感知非线性参数优化作为快速和慢速模式的动态相互作用.
- 采用在飞行中监督的机器学习协议来减少优化子空间.
- 调整机器学习模型以捕获杂的NISQ设备数据.
主要成果:
- 对参数更新所需的量子测量次数显著减少.
- 在计算基态能量方面保持了准确性.
- 证明了拟议方法的分析和数值验证.
- 机器学习模型显示了NISQ设备固有的对噪声的弹性.
结论:
- 拟议的机器学习增强方法大大降低了混合算法的量子测量开销.
- 这种方法加速了NISQ硬件上的分子基态能量计算.
- 这种方法对噪声来说是准确和坚固的,为更高效的量子模拟铺平了道路.
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