Jan 16, 2018 · A QWNN learns a quantum walk on a graph to construct a diffusion operator which can then be applied to graph-structured data. We demonstrate the ...
A QWNN learns a quantum walk on a graph to construct a diffusion operator which can then be applied to graph- structured data. We demonstrate the use of this ...
We show that our quantum walk based neural network approach obtains better or competitive results when compared to other state-of-the-art graph neural network ...
Jun 15, 2018 · We show that our quantum walk based neural network approach obtains better or competitive results when compared to other state-of-the-art graph ...
Sep 23, 2019 · In this work, we propose a novel quantum walk based neural network structure that can be applied to graph data. Quantum random walks differ from ...
We show that our quantum walk based neural network approach obtains competitive results when compared to other graph neural net- work approaches, suggesting ...
Sep 9, 2024 · A QWNN learns a quantum walk on a graph to construct a diffusion operator which can be applied to a signal on a graph. We demonstrate the use of ...
Sep 30, 2024 · A quantum walk neural network is based on learning the coin operators that determine the behavior of quantum random walks.
We propose quantum walk neural networks (QWNN), a novel graph neural network architecture based on quantum random walks, the quantum parallel to classical ...
Jul 10, 2023 · Here we develop an approach that exploits the quantum source's graph structure to improve learning via an arbitrary quantum neural network (QNN) ansatz.
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