基于梯度的优化用于量子架构的搜索
Zhimin He1, Jiachun Wei2, Chuangtao Chen3
1School of Electronic and Information Engineering, Foshan University, Foshan, 528000, China.
概括
我们介绍了基于梯度的量子架构搜索优化 (GQAS),这是设计量子电路的新方法. GQAS有效地搜索连续空间,优于变量量子算法的现有离散方法.
科学领域:
- 量子计算是一种量子计算.
- 人工智能的人工智能
- 算法设计 算法设计
背景情况:
- 量子架构搜索 (QAS) 对于优化变量量子算法 (VQA) 中的量子电路至关重要.
- 目前的QAS方法由于探索离散电路空间,因此效率低下.
- 需要更高效,更可扩展的QAS技术.
研究的目的:
- 提出基于梯度的量子架构搜索优化 (GQAS),这是量子电路设计的新方法.
- 为了使量子电路架构在连续隐性空间中得到有效的探索.
- 通过优化电路设计来提高变量量子算法的性能.
主要方法:
- 开发了一个使用电路编码器,解码器和预测器的GQAS框架.
- 使用编码器嵌入量子架构到连续的潜空间中.
- 通过梯度下降优化潜伏表示,并使用解码器将其映射回离散架构.
- 在一个大数据集的电路架构上使用自主监督学习 (SSL) 预训练编码器.
主要成果:
- 与现有的差异化量子架构搜索 (DQAS) 方法相比,GQAS表现出卓越的性能.
- 在变量量子Eigensolver (VQE) 上的模拟验证了拟议方法的有效性.
- 持续的潜空间优化被证明比离散的搜索方法更有效.
结论:
- GQAS提供了一种更有效和有效的方法来设计用于VQAs的量子电路.
- 在连续潜伏空间中提出的基于梯度的方法代表了QAS的重大进步.
- 这项工作为设计更复杂,更高性能量子电路铺平了道路.
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