A review of quantum neural networks: methods, models, dilemma
R Zhao, S Wang - arXiv preprint arXiv:2109.01840, 2021 - arxiv.org
The rapid development of quantum computer hardware has laid the hardware foundation for
the realization of QNN. Due to quantum properties, QNN shows higher storage capacity and …
the realization of QNN. Due to quantum properties, QNN shows higher storage capacity and …
Stability and generalization of graph convolutional neural networks
Inspired by convolutional neural networks on 1D and 2D data, graph convolutional neural
networks (GCNNs) have been developed for various learning tasks on graph data, and have …
networks (GCNNs) have been developed for various learning tasks on graph data, and have …
Graphqntk: quantum neural tangent kernel for graph data
Abstract Graph Neural Networks (GNNs) and Graph Kernels (GKs) are two fundamental
tools used to analyze graph-structured data. Efforts have been recently made in developing …
tools used to analyze graph-structured data. Efforts have been recently made in developing …
A pseudorandom number generator based on the chaotic map and quantum random walks
W Zhao, Z Chang, C Ma, Z Shen - Entropy, 2023 - mdpi.com
In this paper, a surjective mapping that satisfies the Li–Yorke chaos in the unit area is
constructed and a perturbation algorithm (disturbing its parameters and inputs through …
constructed and a perturbation algorithm (disturbing its parameters and inputs through …
Quantum 3D graph learning with applications to molecule embedding
Learning 3D graph with spatial position as well as node attributes has been recently actively
studied, for its utility in different applications eg 3D molecules. Quantum computing is known …
studied, for its utility in different applications eg 3D molecules. Quantum computing is known …
Vqne: Variational quantum network embedding with application to network alignment
Learning of network embedding with vector-based node representation has attracted wide
attention over the decade. It differs from the general setting of graph node embedding …
attention over the decade. It differs from the general setting of graph node embedding …
A Machine Learning Study of High Robustness Quantum Walk Search Algorithm with Qudit Householder Coins
In this work, the quantum random walk search algorithm with a walk coin constructed by
generalized Householder reflection and phase multiplier has been studied. The coin register …
generalized Householder reflection and phase multiplier has been studied. The coin register …
Quantum walk neural networks with feature dependent coins
S Dernbach, A Mohseni-Kabir, S Pal, M Gepner… - Applied Network …, 2019 - Springer
Recent neural networks designed to operate on graph-structured data have proven effective
in many domains. These graph neural networks often diffuse information using the spatial …
in many domains. These graph neural networks often diffuse information using the spatial …
Application of Deep Learning Neural Networks in Computer-Aided Drug Discovery: A Review
JS Mathivanan, VV Dhayabaran, MR David… - Current …, 2024 - benthamdirect.com
Computer-aided drug design has an important role in drug development and design. It has
become a thriving area of research in the pharmaceutical industry to accelerate the drug …
become a thriving area of research in the pharmaceutical industry to accelerate the drug …
Quantum technologies: Digital transformation, social impact, and cross-sector disruption
MA López, MM Da Silva - 2019 - publications.iadb.org
Quantum technologies have been around since the middle of the 20th century and, for the
past 60 years, they have contributed to small-scale developments and improvements …
past 60 years, they have contributed to small-scale developments and improvements …