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Lecture notes in networks and systems

A Real Quantum Improvement Research Quantum Computing Algorithms Defining Entanglement and Computation

V. Karthick, A. Gayathri

Published Jan 1, 2025• 0 citations
Publisher
DOI
10.1007/978-981-97-7178-3_41

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This paper explores the potential of topological correlation quantum entanglement calculation models to investigate the properties of quantum entanglement and design quantum computation algorithms. The core concept is to leverage the unique properties of topological correlations to define the measurement outcomes of quantum entanglement, offering a novel approach to studying entanglement and potentially leading to improved quantum algorithms. We propose a new model based on this principle, aiming to advance our understanding of entanglement and to stimulate the development of new quantum computing techniques. This work presents a theoretical framework for utilizing topological correlations to analyze entanglement phenomena and serves as a foundation for future research in this field.

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This paper introduces a novel quantum computing constraint optimization algorithm designed to address the challenges posed by large-scale constraint optimization problems. Traditional methods often struggle with the computational complexity of these problems, while this algorithm leverages quantum entanglement and quantum algorithms to significantly enhance efficiency and computational power. The core mechanism involves transforming the optimization problem into a quantum algorithm, exploiting quantum properties to improve algorithm performance. This research explores the potential of quantum computing to revolutionize the field of constraint optimization, offering a promising approach to tackling complex real-world problems.
Today's robots are controlled with classical computers that make decisions based on algorithms designed for a classical computing architecture.However, the amount of data increases continuously -a fact which pushes classical computers that control these robots to their limits.In comparison to classical computing, fundamentally different concepts of physics are the basis of quantum computing and thereby quantum computing has the potential to solve specific computational problems more efficiently.In addition to the aforementioned complications, the subgroup of robots that are subject to the research of this paper, namely humanoid robots, not only need more computational capacity but also require an algorithmic design based on a behavioral model which avoids causing antipathy when interacting with human beings.Using a real quantum computer with quantum phenomena such as superposition, entanglement, and quantum parallelism, new behavioral models and algorithms can be designed to improve human robot interaction and speed up machine learning processes.This paper promotes a hybrid-controlled robot using the combination of a classical computer-and quantum computer.With four quantum circuits to define a behavioral model and three different quantum machine learning algorithms we show the advantage of the hybrid-controlled robot.
This paper explores the potential of topological correlation quantum entanglement calculation models to investigate the properties of quantum entanglement and design quantum computation algorithms. The core concept is to leverage the unique properties of topological correlations to define the measurement outcomes of quantum entanglement, offering a novel approach to studying entanglement and potentially leading to improved quantum algorithms. We propose a new model based on this principle, aiming to advance our understanding of entanglement and to stimulate the development of new quantum computing techniques. This work presents a theoretical framework for utilizing topological correlations to analyze entanglement phenomena and serves as a foundation for future research in this field.