在数字亚亚巴特状态准备中,托特错误的自我修复
Lucas K Kovalsky1, Fernando A Calderon-Vargas1, Matthew D Grace1
1Quantum Algorithms and Applications Collaboratory, Sandia National Laboratories, Livermore, California 94550, USA.
Physical review letters
|August 25, 2023
概括
使用Trotterization的数字量子模拟可以准备复杂的状态. 第一阶段的Trotterization显示出更好的不忠度缩放,O(T^{-2}δt^{2}),这表明数字化量子化中的自我修复机制.
科学领域:
- 量子信息科学 量子信息科学
- 量子多体物理学 量子多体物理学
- 计算量子物理 计算量子物理
背景情况:
- 阿迪亚巴特时间进化是合成复杂量子多体状态的关键.
- 阿迪亚巴特进化的数字化实现使用Trotterization.
- 非对话性和数字化在这个过程中引入了不忠.
研究的目的:
- 分析第一阶级Trotterization的累积不忠,以实现完整的亚亚巴特进化.
- 通过时间步骤 (δt) 和总时间 (T) 来研究不忠的缩放.
- 了解数字化量子和量子近似优化算法之间的关系.
主要方法:
- 在Trotterization下对阿迪亚巴特时间演化的理论分析.
- 对第一阶段的Trotterization进行不忠度缩放的推导.
- 与一般的Trotter误差界限进行比较.
主要成果:
- 累积的不忠度为O(T^{-2}δt^{2}),比预期的O(T^{2}δt^{2}有所改善.
- 这种缩放表明数字化亚亚巴特进化的自我愈合机制.
- 不忠度随着固定时间步骤 (δt) 的总时间 (T) 的增加而减少.
结论:
- 这些发现解释了对数字化进化的不忠观察到的下降.
- 在量子近似优化算法和数字化量子化之间建立了对应.
- 该研究提供了对数字量子模拟效率的理论见解.
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