用随机重新配置和线性方法对相关电子状态进行变量优化的量子算法
Mario Motta1, Kevin J Sung1, James Shee2
1IBM Quantum, IBM T.J. Watson Research Center, Yorktown Heights, New York 10598, United States.
The journal of physical chemistry. A
|September 30, 2024
概括
我们开发了用于优化波函数的量子算法,大大降低了强相关电子状态的计算成本. 这些方法,包括线性方法,对于具有挑战性的分子系统,如N2和C2二次体,实现了高精度.
科学领域:
- 量子计算是一种量子计算.
- 计算化学是一种计算化学.
- 电子结构理论 电子结构理论
背景情况:
- 对于强烈相关的系统来说,解决电子施罗丁格方程的计算要求很高.
- 波函数的变量优化对于准确的电子结构计算至关重要.
- 现有的经典方法面临着复杂系统的指数级扩展挑战.
研究的目的:
- 提出用于变量波函数优化的新型量子算法.
- 将这些算法应用于使用LUCJ (Local Unitary Cluster Jastrow) 的强烈相关的基本状态.
- 将量子启发的优化技术与经典方法进行比较.
主要方法:
- 开发使用随机重新配置 (SR) 和线性方法 (LM) 的量子算法.
- 用单元运算符相关联的波函数实现变量优化.
- 经典模拟用于将优化性能与L-BFGS-B.比较.
主要成果:
- 量子算法与系统大小呈现多项式缩放,与指数式经典成本不同.
- 线性方法 (LM) 对于N2和C2解离曲线的能量总是低于L-BFGS-B.
- LUCJ的替代预测显示出高准确度,偏差≤1 kcal/mol与精确的对角化.
结论:
- 优化技术对于解决古典和量子硬件上的电子结构问题至关重要.
- 拟议的量子算法为强烈相关的电子系统提供了可扩展的方法.
- 需要量子友好的解决方案,如对称投射的分析和受约束的优化,以使潜在能量曲线平滑.
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