.png){kind=icon}
返回
Matrix product state pre-training for quantum machine learning
.png){kind=icon}
OA
AI
DOI：10.1088/2058-9565/ac7073.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Hybrid quantum-classical algorithms are a promising candidate for developing uses for NISQ devices. In particular, parametrised quantum circuits (PQCs) paired with classical optimizers have been used as a basis for quantum chemistry and quantum optimization problems. Tensor network methods are being increasingly used as a classical machine learning tool, as well as a tool for studying quantum systems. We introduce a circuit pre-training method based on matrix product state machine learning methods, and demonstrate that it accelerates training of PQCs for both supervised learning, energy minimization, and combinatorial optimization.
Keyword:
quantum
machine learning
NLSQ
matrix produce state
VQE
期刊
IF:
5
论文数:
1.4K
被引数:
5.1K
机构
引用论文
.png){kind=icon}
查看更多内容