在量子硬件上解决线性系统,使用混合HHL+
Romina Yalovetzky1, Pierre Minssen2, Dylan Herman2
1Global Technology Applied Research, JPMorganChase, New York, NY, 10017, USA. romina.yalovetzky@jpmorgan.com.
Scientific reports
|September 10, 2024
概括
这项研究引入了对量子线性代数的改进的混合哈罗-哈西迪姆-劳埃德 (HHL) 算法,使其与当前的量子硬件更兼容. 改进的算法成功地证明了其在捕获离子量子计算机上解决投资组合优化问题的有效性.
科学领域:
- 量子计算是一种量子计算.
- 量子算法 量子算法 量子算法
- 量子线性代数 量子线性代数
背景情况:
- 当前量子硬件的局限性限制了量子算法演示和面向应用的基准测试的规模.
- 哈罗-哈西迪姆-劳埃德 (HHL) 算法,一个关键的量子线性代数原始,在很大程度上无法访问杂的中间尺度量子 (NISQ) 设备,需要混合的古典量子方法.
研究的目的:
- 为了弥合近期友好的HHL实现与当前噪音严重的硬件上的可执行量子电路之间的差距.
- 为了提高混合HHL算法与现有的量子设备的兼容性.
- 为了实践应用,使HHL能够进行更大规模的实验演示.
主要方法:
- 建议对混合HHL算法的两个修改:一种用于确定线性系统矩阵的缩放因子的新方法,以优化辅助量子比特在相位估计中的使用,以及用于电路压缩的启发式.
- 在量子系统模型H系列被困离子量子计算机上实现和执行修改后的混合HHL算法.
主要成果:
- 在真实量子硬件上成功证明了修改后的混合HHL算法的有效性.
- 解决了HHL相位估计组件中有限的辅助量子位的挑战.
- 实现了迄今为止HHL在一个应用程序 (投资组合优化) 中最大的实验演示.
结论:
- 拟议的修改显著提高了当前量子硬件的HHL算法的实用性.
- 增强的混合HHL算法与NISQ设备更兼容,促进了更广泛的面向应用的基准测试.
- 这项工作代表了利用量子线性代数原体在现有量子计算机上解决现实问题的重要一步.
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