Zobrazeno 1 - 10
of 52
pro vyhledávání: '"Lu, Sirui"'
We introduce a variational Monte Carlo algorithm for approximating finite-temperature quantum many-body systems, based on the minimization of a modified free energy. We employ a variety of trial states -- both tensor networks as well as neural networ
Externí odkaz:
http://arxiv.org/abs/2401.14243
Publikováno v:
In SSM - Mental Health December 2024 6
Publikováno v:
Science China Physics, Mechanics & Astronomy, 64, 100312 (2021)
Private distributed learning studies the problem of how multiple distributed entities collaboratively train a shared deep network with their private data unrevealed. With the security provided by the protocols of blind quantum computation, the cooper
Externí odkaz:
http://arxiv.org/abs/2103.08403
We investigate the potential of tensor network based machine learning methods to scale to large image and text data sets. For that, we study how the mutual information between a subregion and its complement scales with the subsystem size $L$, similar
Externí odkaz:
http://arxiv.org/abs/2103.06872
Publikováno v:
PRX Quantum 2, 020321 (2021)
We introduce two kinds of quantum algorithms to explore microcanonical and canonical properties of many-body systems. The first one is a hybrid quantum algorithm that, given an efficiently preparable state, computes expectation values in a finite ene
Externí odkaz:
http://arxiv.org/abs/2006.03032
Publikováno v:
Phys. Rev. Research 2, 033212 (2020)
Adversarial machine learning is an emerging field that focuses on studying vulnerabilities of machine learning approaches in adversarial settings and developing techniques accordingly to make learning robust to adversarial manipulations. It plays a v
Externí odkaz:
http://arxiv.org/abs/2001.00030
Publikováno v:
Quantum Front 2, 15 (2023)
We study the robustness of machine learning approaches to adversarial perturbations, with a focus on supervised learning scenarios. We find that typical phase classifiers based on deep neural networks are extremely vulnerable to adversarial perturbat
Externí odkaz:
http://arxiv.org/abs/1910.13453
The quantum approximate optimization algorithm~(QAOA) first proposed by Farhi et al. promises near-term applications based on its simplicity, universality, and provable optimality. A depth-p QAOA consists of p interleaved unitary transformations indu
Externí odkaz:
http://arxiv.org/abs/1905.12134
Autor:
Lian, Wenqian, Wang, Sheng-Tao, Lu, Sirui, Huang, Yuanyuan, Wang, Fei, Yuan, Xinxing, Zhang, Wengang, Ouyang, Xiaolong, Wang, Xin, Huang, Xianzhi, He, Li, Chang, Xiuying, Deng, Dong-Ling, Duan, Lu-Ming
Publikováno v:
Phys. Rev. Lett. 122, 210503 (2019)
We report an experimental demonstration of a machine learning approach to identify exotic topological phases, with a focus on the three-dimensional chiral topological insulators. We show that the convolutional neural networks---a class of deep feed-f
Externí odkaz:
http://arxiv.org/abs/1905.03255
Publikováno v:
New J. Phys. 22 (2020) 083088
Local Hamiltonians arise naturally in physical systems. Despite its seemingly `simple' local structure, exotic features such as nonlocal correlations and topological orders exhibit in eigenstates of these systems. Previous studies for recovering loca
Externí odkaz:
http://arxiv.org/abs/1903.06569