GASP:一种用于量子计算机状态准备的遗传算法
Floyd M Creevey1, Charles D Hill1,2, Lloyd C L Hollenberg3
1School of Physics, University of Melbourne, Melbourne, 3010, Australia.
Scientific reports
|July 24, 2023
概括
我们开发了一个状态准备 (GASP) 的遗传算法,以创建高效的,低深度的量子电路,用于初始化量子计算机. GASP的性能优于现有的方法,减少了用于高准确度合成量子状态的门数.
科学领域:
- 量子计算是一种量子计算.
- 量子信息科学 量子信息科学
- 算法开发 算法开发
背景情况:
- 高效的量子状态准备对于量子算法至关重要,特别是在有限的量子资源的NISQ时代.
- 低深度量子电路对于当前噪音中等尺度量子 (NISQ) 设备的实现至关重要.
- 现有的状态准备方法在资源效率和电路深度方面面临挑战.
研究的目的:
- 引入一种新的状态准备 (GASP) 遗传算法,以生成高效,低深度的量子电路.
- 为了使量子计算机能够以高保真性初始化到指定的量子状态.
- 为了比较GASP的性能与既定的状态初始化技术.
主要方法:
- 使用基因算法,其基础是单量子位旋转和 CNOT 门.
- 系统地生成量子电路以将目标状态合成到所需的保真度.
- 在模拟和物理量子设备上实施和比较GASP与IBM Qiskit的精确合成方法.
主要成果:
- 与其他方法相比,GASP产生了具有明显较低深度和门数量的量子电路.
- 该算法在合成各种量子状态方面表现出卓越的性能,包括高斯和W状态.
- 结果显示,准确状态准备所需的门数量持续减少.
结论:
- GASP为量子状态准备提供了更有效的方法,这对于推进NISQ算法至关重要.
- 该方法能够产生较低深度的电路,从而减轻错误的积累,提高了实现的可靠性.
- GASP为量子计算中现有的状态初始化技术提供了具有竞争力和有效的替代方案.
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