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对Max-Cut问题进行启发式Floquet增值算法的基准测试

Etienne Granet1, Henrik Dreyer2

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概括
此摘要是机器生成的。

对于优化问题,Floquet 增值进化提供了一种更有效的量子计算方法. 这种方法显著减少了门数,为量子计算机上的Max-Cut等问题提供了最佳的解决方案.

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科学领域:

  • 量子力学
  • 计算科学
  • 优化算法

背景情况:

  • 量子力学的阿迪亚巴斯定理指出,一个系统在缓慢的哈密尔顿变化下保持其基本状态.
  • 阿迪亚巴特量子计算原理可以解决复杂的问题,但由于Trotter步骤缩放,通常需要数字量子计算机上的大门数量.

研究的目的:

  • 为了研究一种新的方法,Floquet的亚亚巴特进化,在数字量子计算机上有效地实现亚亚巴特动力学.
  • 为了证明Floquet增量进化的有效性来解决经典的优化问题,特别是Max-Cut问题.

主要方法:

  • 建议使用固定的,有限的Trotter步骤来实现Floquet的平移动态演变.
  • 使用矩阵产品状态模拟来提供方法有效性的数值证据.
  • 在Max-Cut问题上测试了该方法.

主要成果:

  • 与连续时间的亚亚巴特进化相比,Floquet的亚亚巴特进化显著减少了几个数量级的门数.
  • 数字模拟显示了低运行时间和粘合尺寸的3规律图上的Max-Cut问题的最佳解决方案.
  • 资源估计表明量子计算机在解决这个问题上的表现可能会优于经典的解决方案.

结论:

  • 弗洛克特的亚亚巴特进化为亚亚巴特量子计算提供了一个计算效率高的替代方案.
  • 这种方法在近期量子设备上有望解决像Max-Cut这样的难度优化问题.