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pro vyhledávání: '"Ran, Shi"'
Simulating strongly-correlated quantum systems in continuous space belongs to the most challenging and long-concerned issues in quantum physics. This work investigates the quantum entanglement and criticality of the ground-state wave-functions of inf
Externí odkaz:
http://arxiv.org/abs/2410.23624
Autor:
Bai, Sheng-Chen, Ran, Shi-Ju
Interpreting the representation and generalization powers has been a long-standing issue in the field of machine learning (ML) and artificial intelligence. This work contributes to uncovering the emergence of universal scaling laws in quantum-probabi
Externí odkaz:
http://arxiv.org/abs/2410.09703
Autor:
Bai, Sheng-Chen, Ran, Shi-Ju
Replicating chaotic characteristics of non-linear dynamics by machine learning (ML) has recently drawn wide attentions. In this work, we propose that a ML model, trained to predict the state one-step-ahead from several latest historic states, can acc
Externí odkaz:
http://arxiv.org/abs/2405.08484
Autor:
Ran, Shi-Ju, Su, Gang
Publikováno v:
Intelligent Computing 2, 0061 (2023)
It is a critical challenge to simultaneously gain high interpretability and efficiency with the current schemes of deep machine learning (ML). Tensor network (TN), which is a well-established mathematical tool originating from quantum mechanics, has
Externí odkaz:
http://arxiv.org/abs/2311.11258
Autor:
Wang, Ding-Zu, Zhu, Hao, Cui, Jian, Argüello-Luengo, Javier, Lewenstein, Maciej, Zhang, Guo-Feng, Sierant, Piotr, Ran, Shi-Ju
The eigenstate thermalization hypothesis (ETH) is a successful theory that establishes the criteria for ergodicity and thermalization in isolated quantum many-body systems. In this work, we investigate the thermalization properties of spin-$ 1/2 $ XX
Externí odkaz:
http://arxiv.org/abs/2310.19333
Quantum state tomography (QST) is plagued by the ``curse of dimensionality'' due to the exponentially-scaled complexity in measurement and data post-processing. Efficient QST schemes for large-scale mixed states are currently missing. In this work, w
Externí odkaz:
http://arxiv.org/abs/2308.06900
Publikováno v:
Phys. Rev. Lett. 133, 070402 (2024)
Entanglement propagation provides a key routine to understand quantum many-body dynamics in and out of equilibrium. The entanglement entropy (EE) usually approaches to a sub-saturation known as the Page value $\tilde{S}_{P} =\tilde{S} - dS$ (with $\t
Externí odkaz:
http://arxiv.org/abs/2307.11609
Publikováno v:
Phys. Rev. B 107, L220401 (2023)
The nature of the 1/9-magnetization plateau of the spin-1/2 kagome Heisenberg antiferromagnet remains controversial due to the exotic physical properties and high complexity induced by the geometrical frustration. Instead of a Z3 quantum spin liquid
Externí odkaz:
http://arxiv.org/abs/2306.09563
Neural network (NN) designed for challenging machine learning tasks is in general a highly nonlinear mapping that contains massive variational parameters. High complexity of NN, if unbounded or unconstrained, might unpredictably cause severe issues i
Externí odkaz:
http://arxiv.org/abs/2305.06058
Autor:
An, Yu-Jia, Bai, Sheng-Chen, Cheng, Lin, Li, Xiao-Guang, Wang, Cheng-en, Han, Xiao-Dong, Su, Gang, Ran, Shi-Ju, Wang, Cong
Artificial intelligence (AI) has brought tremendous impacts on biomedical sciences from academic researches to clinical applications, such as in biomarkers' detection and diagnosis, optimization of treatment, and identification of new therapeutic tar
Externí odkaz:
http://arxiv.org/abs/2303.06340