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通过量子化进行近似优化的缩放优势.

Humberto Munoz-Bauza1,2, Daniel Lidar3,4,5,6

  • 1NASA Ames Research Center, Quantum Artificial Intelligence Lab. (QuAIL), Moffett Field, California 94035, USA.

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概括

量子化在近似优化方面显示了扩展优势,超过了经典算法. 这种量子回火校正 (QAC) 方法可以加速在复杂问题中找到低能量的状态.

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科学领域:

  • 量子计算是一种量子计算.
  • 计算物理 计算物理
  • 优化算法 优化算法

背景情况:

  • 量子化器正在为复杂的优化和模拟而发展.
  • 在精确优化的量子优势仍然难以捉摸.
  • 大致优化是证明量子效益的关键领域.

研究的目的:

  • 为了证明在近似优化中量子化缩放的优势.
  • 为了比较量子回火性能与领先的经典算法 (PT-ICM).
  • 引入和验证量子回火校正 (QAC) 以消除错误.

主要方法:

  • 通过使用错误纠正代码实现了量子回火校正 (QAC).
  • 使用了D-Wave Advantage量子化器,拥有超过1300个逻辑量子比特.
  • 在2D旋转玻璃问题上的基准性能使用时间到epsilon度量.
  • 与以同能集群移动 (PT-ICM) 进行平行炼相比.

主要成果:

  • 使用QAC进行量子化证明了比PT-ICM具有扩展优势.
  • 在采样低能耗状态中实现了加快速度,最佳性差距至少为1.0%.
  • 展示了QAC在抑制量子化错误方面的有效性.

结论:

  • 这项研究介绍了在近似优化中的第一个算法量子加速度.
  • 由QAC增强的量子化为解决复杂的优化挑战提供了一条可行的途径.
  • 错误抑制技术对于实现硬件化中的量子优势至关重要.