Quantum-Inspired Deep Learning for Networked Data Analysis With Quantum Networked Discord and Allies

Abstract

This research talks about Quantum Networked Discord and Allies (QNDA), a new way to get insights from linked datasets. It combines methods inspired by quantum mechanics with network analysis and deep learning. Our method improves the processing of huge amounts of networked data by using the inherent parallelism and superposition properties. It is based on ideas from quantum computing. We also came up with the idea of Quantum Networked Allies (QNA), which uses quantum annealing and coupling to find ways that different parts of networked data can work together in a way that is beneficial. In the end, this leads to better classification and prediction skills. We also provide theoretical analyses to help you understand how the speed improvements seen in QNDA work. In conclusion, our research opens up new ways to deal with the problems that come up with data ecosystems that are very closely linked to each other. It also marks a big step forward in using quantum-inspired methods for analysing data in networks.