.png){kind=icon}
返回
Decohering tensor network quantum machine learning models
.png){kind=icon}
OA
AI
DOI：10.1007/s42484-022-00095-9.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Tensor network quantum machine learning (QML) models are promising applications on near-term quantum hardware. While decoherence of qubits is expected to decrease the performance of QML models, it is unclear to what extent the diminished performance can be compensated for by adding ancillas to the models and accordingly increasing the virtual bond dimension of the models. We investigate here the competition between decoherence and adding ancillas on the classification performance of two models, with an analysis of the decoherence effect from the perspective of regression. We present numerical evidence that the fully decohered unitary tree tensor network (TTN) with two ancillas performs at least as well as the non-decohered unitary TTN, suggesting that it is beneficial to add at least two ancillas to the unitary TTN regardless of the amount of decoherence may be consequently introduced.
Keyword:
Tensor networks
Decoherence
Quantum machine learning
期刊
Q
IF:
4.4
论文数:
451
被引数:
796
机构
引用论文
.png){kind=icon}
分享收藏分享收藏分享收藏分享收藏Nano-scale investigation of granular neoblastic zircon, Vredefort impact structure, South Africa: Evidence for complete shock melting南非Vredefort冲击结构粒状新碎屑锆石的纳米级研究: 完全休克熔化的证据
PREAI
分享收藏
查看更多内容