The Quantum Graph Recurrent Neural Network
Pramoda Medisetty, Leela Krishna Kumar Pallapothu, Poorna Chand Evuru, Kolla Bhanu Prakash, Veda Manohara Sunanda Vulavalapudi, G. P. Saradhi Varma · 2023
Machine learning that combines the power of graph neural networks has its own significance in developing quick report analysis for various dynamic data which traditional statistical models may not be able to accomplish. Alongside the use of Quantum Computing in Neural Networks have become increasingly important over the years due to their ability to learn and process complex patterns. The Importance of QNN lies in the approaches it follows like entanglement, interference multiple super positional states, parallel computing, and backpropagation trainings which cannot be possible for Classical Neural Networks to work with. Also, the QNN, GNN integrates with the Quantum Machine Learning techniques like PennyLane that acts as an OpenShift framework to execute the optimized, trained algorithms for quick computational outcomes. This paper contains the use of Quantum Neural Networking, Quantum Machine Learning.