Quantum Graph Convolutional Neural Networks

Zheng, Jiangbin; Gao, Qing; Lü, Yanxuan

doi:10.23919/ccc52363.2021.9550372

Cited by 18 publications

(10 citation statements)

References 12 publications

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“…Again, their ansatz is based on alternating layers of Hamiltonians, where one Hamiltonian in each layer encodes the problem graph, while a second parametrized Hamiltonian is trained to solve a given problem. A proposal for a quantum graph convolutional NN was made in [31], and the authors of [32] propose directly encoding the adjacency matrix of a graph into a unitary to build a quantum circuit for graph convolutions. While all of the above works introduce forms of structured QML models, none of them study their properties explicitly from a geometric learning perspective or relate their performance to unstructured ansatzes.…”

Section: A Geometric Learning -Quantum and Classicalmentioning

confidence: 99%

Equivariant quantum circuits for learning on weighted graphs

Skolik¹,

Cattelan²,

Yarkoni³

et al. 2022

Preprint

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Section: A Geometric Learning -Quantum and Classicalmentioning

confidence: 99%

Equivariant quantum circuits for learning on weighted graphs

Skolik¹,

Cattelan²,

Yarkoni³

et al. 2022

Preprint

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“…This is consistent with the reported ability of DNN to map Feynman's path integrals. 202 In quantum computing version of CNNs, convolutional and pooling operations could be performed using a series of qubit (Figure 3) unitary operations e −iHt yielding Bell states 203 which exhibit nonlocal correlations, 204 after which a fully connected layer is applied using single and two-qubit gates. 205 Finally, path integral inter-pretation connects CNN with instanton, a periodic classical orbit in imaginary time, whose exponential of negative action along trajectory approximates the reaction rate constant.…”

Section: From Mixture To Propertymentioning

confidence: 99%

Mixtures Recomposition by Neural Nets: A Multidisciplinary Overview

Nicolle,

Deng,

Ihme

et al. 2024

J. Chem. Inf. Model.

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Artificial Neural Networks (ANNs) are transforming how we understand chemical mixtures, providing an expressive view of the chemical space and multiscale processes. Their hybridization with physical knowledge can bridge the gap between predictivity and understanding of the underlying processes. This overview explores recent progress in ANNs, particularly their potential in the ’recomposition’ of chemical mixtures. Graph-based representations reveal patterns among mixture components, and deep learning models excel in capturing complexity and symmetries when compared to traditional Quantitative Structure–Property Relationship models. Key components, such as Hamiltonian networks and convolution operations, play a central role in representing multiscale mixtures. The integration of ANNs with Chemical Reaction Networks and Physics-Informed Neural Networks for inverse chemical kinetic problems is also examined. The combination of sensors with ANNs shows promise in optical and biomimetic applications. A common ground is identified in the context of statistical physics, where ANN-based methods iteratively adapt their models by blending their initial states with training data. The concept of mixture recomposition unveils a reciprocal inspiration between ANNs and reactive mixtures, highlighting learning behaviors influenced by the training environment.

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“…Zheng et al (2021) [ 78 ] created a model with state preparation, quantum graph convolution, quantum pooling, and quantum measurements. During state preparation, the node features are encoded into a quantum register per node, and the connectivity is encoded as |0〉 or |1〉 on the node-pair-representing qubits.…”

Section: Related Workmentioning

confidence: 99%

Quantum Graph Neural Network Models for Materials Search

Ryu,

Elala,

Rhee

2023

Materials

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Inspired by classical graph neural networks, we discuss a novel quantum graph neural network (QGNN) model to predict the chemical and physical properties of molecules and materials. QGNNs were investigated to predict the energy gap between the highest occupied and lowest unoccupied molecular orbitals of small organic molecules. The models utilize the equivariantly diagonalizable unitary quantum graph circuit (EDU-QGC) framework to allow discrete link features and minimize quantum circuit embedding. The results show QGNNs can achieve lower test loss compared to classical models if a similar number of trainable variables are used, and converge faster in training. This paper also provides a review of classical graph neural network models for materials research and various QGNNs.

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Quantum Graph Convolutional Neural Networks

Cited by 18 publications

References 12 publications

Equivariant quantum circuits for learning on weighted graphs

Equivariant quantum circuits for learning on weighted graphs

Mixtures Recomposition by Neural Nets: A Multidisciplinary Overview

Quantum Graph Neural Network Models for Materials Search

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