Neural network representation for minimally entangled typical thermal states

Hendry, Douglas; Chen, Hongwei; Feiguin, Adrian

doi:10.1103/physrevb.106.165111

Cited by 9 publications

(4 citation statements)

References 62 publications

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“…Thus, we conclude that the quantum oscillator model can reproduce the predictions of the neural network model provided that the outputs of the single and double parabolic potential well oscillators are combined together, which can be achieved, for example, by coupling them into a chain oscillator. While the discussion of an implementation of this approach is beyond the scope of this paper, the similarity of the outputs of the neural network model and the quantum oscillator model has a clear physical meaning: both models are dynamical systems that operate according to the fundamental laws of quantum mechanics [99][100][101].…”

Section: Neural Network Model Versus Quantum Oscillator Modelmentioning

confidence: 99%

Quantum-Inspired Neural Network Model of Optical Illusions

Maksymov

2024

Algorithms

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Ambiguous optical illusions have been a paradigmatic object of fascination, research and inspiration in arts, psychology and video games. However, accurate computational models of perception of ambiguous figures have been elusive. In this paper, we design and train a deep neural network model to simulate human perception of the Necker cube, an ambiguous drawing with several alternating possible interpretations. Defining the weights of the neural network connection using a quantum generator of truly random numbers, in agreement with the emerging concepts of quantum artificial intelligence and quantum cognition, we reveal that the actual perceptual state of the Necker cube is a qubit-like superposition of the two fundamental perceptual states predicted by classical theories. Our results finds applications in video games and virtual reality systems employed for training of astronauts and operators of unmanned aerial vehicles. They are also useful for researchers working in the fields of machine learning and vision, psychology of perception and quantum–mechanical models of human mind and decision making.

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Section: Neural Network Model Versus Quantum Oscillator Modelmentioning

confidence: 99%

Quantum-Inspired Neural Network Model of Optical Illusions

Maksymov

2024

Algorithms

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“…Machine learning methods have recently emerged as a valuable tool to study the quantum many-body physics problems [8][9][10][11][12][13][14][15][16][17][18][19][20][21][22]. Its ability to process high dimensional data and recognize complex patterns have been utilized to determine phase diagrams and phase transitions [23][24][25][26][27][28][29][30][31][32][33][34].…”

Section: Introductionmentioning

confidence: 99%

The structure of heavily doped impurity band in crystalline host

Chen¹,

Hu²

2023

Preprint

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We study the properties of the impurity band in heavily-doped non-magnetic semiconductors using the Jacobi-Davidson algorithm and the supervised deep learning method. The disorder averaged inverse participation ratio (IPR) and thouless number calculation show us the rich structure inside the impurity band. A Convolutional Neural Network(CNN) model, which is trained to distinguish the extended/localized phase of the Anderson model with high accuracy, shows us the results in good agreement with the conventional approach. Together, we find that there are three mobility edges in the impurity band for a specific on-site impurity potential, which means the presence of the extended states while filling the impurity band.

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“…We present an approach that expands the spectral function with Chebyshev polynomials and these polynomials are represented by neural network quantum states [93]. Besides, we carry out thermodynamic simulations of quantum many-body models using neural network representation of minimally entangled typical thermal stats [92].…”

Section: Introduction 11 Introductionmentioning

confidence: 99%

“…Machine learning methods have recently emerged as a valuable tool to study the quantum many-body physics problems [34,35,50,229,234,247,178,131,100,93,49,171,199,92,41,141,78,172]. Its ability to process high dimensional data and recognize complex patterns have been utilized to determine phase diagrams and phase transitions [237,165,223,142,29,192,126,61,115,267,266,121].…”

Section: Introductionmentioning

confidence: 99%

Machine learning and high-performance computing in numerical simulation of quantum many-body systems

Chen

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Neural network representation for minimally entangled typical thermal states

Cited by 9 publications

References 62 publications

Quantum-Inspired Neural Network Model of Optical Illusions

Quantum-Inspired Neural Network Model of Optical Illusions

The structure of heavily doped impurity band in crystalline host

Machine learning and high-performance computing in numerical simulation of quantum many-body systems

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