介绍用于变量量子算法的内核下降优化器
Lars Simon1, Holger Eble1, Manuel Radons2
1Bundesdruckerei GmbH, Kommandantenstraße 18, 10969, Berlin, Germany.
Scientific reports
|August 2, 2025
概括
内核下降是一种新的算法,用于优化近期量子设备上的变量量子算法. 它在关键场景中表现优于梯度下降和量子分析下降.
科学领域:
- 量子计算是一种量子计算.
- 优化算法 优化算法
背景情况:
- 变量量子算法是一种有希望的方法,可以在当前杂的中等尺度量子 (NISQ) 设备上实现量子优势.
- 有效地最小化这些算法的目标函数对于它们的实际应用至关重要.
研究的目的:
- 介绍内核下降,一种用于最小化变量量子算法中的函数的新算法.
- 与现有的优化方法比较内核下降的性能.
- 通过广泛的实验来证明内核下降的有效性.
主要方法:
- 内核下降以代方式计算目标函数的经典局部近似值.
- 它使用复制内核希尔伯特空间技术来构建这些局部近似.
- 在这些近似上执行经典优化步骤.
主要成果:
- 与梯度下降相比,内核下降显示出更高的性能.
- 在特定场景中,内核下降优于量子分析下降.
- 广泛的实验验证了内核下降算法的有效性.
结论:
- 内核下降为优化变量量子算法提供了一种有效的新方法.
- 使用复制内核的希尔伯特空间技术提供了一个明显的优势.
- 这种算法对推进NISQ设备的应用非常有希望.
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