Quantum entanglement and quantum computing

F. J. Duarte

Jincheng Zhang

Quantum entanglement is a cornerstone of quantum computing, offering the potential for unparalleled computational power. However, harnessing its capabilities has been hampered by the inherent complexity of managing and optimizing entangled states. This paper presents an innovative approach to quantum entanglement computation, utilizing an adaptive learning framework to dynamically adjust quantum entanglement parameters. Our work demonstrates a significant improvement in computational efficiency through automated parameter tuning, significantly reducing the time required for entanglement calculations. The proposed mechanism leverages a self-learning algorithm to optimize the entanglement distribution and entanglement duration, thereby unlocking the potential of quantum computing. This research addresses a critical limitation in current quantum computing architectures – the reliance on manual parameter adjustment – and paves the way for more efficient and scalable quantum algorithms.

Amirul Asyraf Zhahir, Siti Munirah Mohd, Mohd Ilias M Shuhud +4 more

Quantum computing is different from classical computing. It is based on quantum mechanics principles, utilizing the attributes such as entanglement and superposition. The significant difference between a quantum computer and a classical computer is the ability to process information astonishingly faster. This is possible due to the quantum entanglement concept. Interests in the quantum entanglement concept have been exponentially growing over the years. In the last decade, significant progress in implementations of quantum computation in quantum information has been achieved. This study selected secondary resource indexed literature from several databases with specific keywords in almost a century. In this paper, quantum computing and its application is presented along with the essence of quantum entanglement role in quantum computing. This study will provide a general understanding of quantum computing and its application and quantum entanglement.

Marcel Patulacci, Mona Lisa

Jincheng Zhang

This paper presents a novel computational framework, "Multi-Quantum Entanglement Computation," designed to accelerate and optimize complex quantum computations. The core concept revolves around leveraging the unique properties of multi-quantum entanglement to enhance computational efficiency and explore novel applications of quantum computing. This framework introduces a dynamic entanglement scheme that adapts to the specific requirements of each quantum algorithm, improving performance across a range of tasks. The research explores the potential of multi-quantum entanglement to overcome limitations inherent in existing quantum computation methods, paving the way for more efficient and versatile quantum algorithms.

Benoît Vermersch