.png){kind=icon}
返回
Improving gradient methods via coordinate transformations: Applications to quantum machine learning
.png){kind=icon}
OA
AI
DOI：10.1103/PhysRevResearch.6.023069.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
In this paper, we introduce a generic strategy to accelerate and improve the overall performance of machinelearning algorithms, both in their classical and quantum versions, heavily rely on optimization algorithms based on gradients, such as gradient descent. The overall performance is dependent on the appearance of local minima and barren plateaus, which slow down calculations and lead to nonoptimal solutions. In practice, this results in dramatic computational and energy costs for artificial intelligence applications. Our method is based on coordinate transformations, like variational rotations, adding extra directions in parameter space that depend on the cost function itself, and which allowus to explore the configuration landscape more efficiently. The validity of our method is benchmarked by boosting several quantum machine-learning algorithms, getting a very significant improvement in their performance.
期刊
IF:
4.2
论文数:
7.6K
被引数:
2.7W
机构
引用论文
Cost function dependent barren plateaus in shallow parametrized quantum circuits.png)
NATURE COMMUNICATIONS
OAAI
NATURE COMMUNICATIONSIF15.7
OAAI分享收藏A practical introduction to tensor networks: Matrix product states and projected entangled pair states张量网络的实用介绍: 矩阵乘积状态和投影纠缠对状态OAAI
OAAI分享收藏分享收藏 没有更多内容
没有更多内容