.png){kind=icon}
返回
Learning algorithm reflecting universal scaling behavior near phase transitions
.png){kind=icon}
OA
AI
DOI：10.1103/PhysRevResearch.4.L022032.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Machine-learning-inspired techniques have emerged as a new paradigm for analysis of phase transitions in quantum matter. In this work, we introduce a supervised learning algorithm for studying critical phenomena from measurement data, neural network scaling, which is based on iteratively training convolutional networks of increasing (spatial) complexity and test it on the transverse field Ising chain and q = 6 Potts model. At the continuous Ising transition, our scaling procedure directly reflects the hallmark of a continuous (second-order) phase transition, divergence of a characteristic length scale. Specifically, we extract a classification length scale by measuring the response of the classification accuracy while varying the largest convolution size (architecture of the network). We observe empirically the scaling exponent of the classification length is consistent with a power law with the correlation length exponent v = 1. Furthermore, we demonstrate the versatility of our algorithm by showing the universal scaling behaviors persist across a variety of measurement bases, including when the order parameter is nonlocal. Finally, we show that the classification length scale remains finite for the q = 6 Potts model, which has a first-order transition and lacks a divergent correlation length.
Keyword:
NEURAL-NETWORKS
MONTE-CARLO
QUANTUM
期刊
IF:
4.2
论文数:
7.6K
被引数:
2.7W
机构
引用论文
Observation of Entangled States of a Fully Controlled 20-Qubit System完全控制的20量子比特系统的纠缠态的观察.png)
PHYSICAL REVIEW X
OAAI
PHYSICAL REVIEW XIF15.7
OAAI分享收藏Neural network setups for a precise detection of the many-body localization transition: Finite-size scaling and limitationsPHYSICAL REVIEW BOAAI
PHYSICAL REVIEW BIF3.7
OAAI分享收藏Photoinduced Molecular Transport in Biological Environments Based on Dipole Moment Fluctuations
PREAI
分享收藏
查看更多内容