Quantum Search Algorithm Research And Calculation Of Quantum Entanglement

Quantum computation and quantum information technology are new subjects, which are developed recently. They have got remarkable progress, and have wide application prospect. With the growth of quantum computation and quantum information technology, computer science is rapidly developing. In the not-far future, quantum information times will be true.The paper summarizes the research background and significance of this article, briefly describes the necessary physical and mathematical knowledge of quantum computation and quantum information research and the basic elements required for quantum computing. Grover quantum search algorithm,which is a challenging research area and speeding search rate of the classical algorithm by square, is one of the most important progress in quantum computation. This paper studies the Grover quantum search algorithm and its improvement ideas. We clarify the search ideas, search process, geometric description, algorithm performance and the main problems of it. Then we describe the realizable method of the rotation phase and the target weight, and compare algorithms with each other, which have different rotation phases.Quantum entanglement phenomenon, which is a unique resource of quantum mechanics, is one of the important foundations of quantum information technology. Quantum entanglement is used as the basis for such applications as quantum cryptography, quantum communication, quantum computer and the next quantum internet. The calculation of quantum entanglement therefore gains importance. But with the evolution of application systems, the calculation of quantum entanglement has become more complex. This paper develops a method for the calculation of entanglement, which uses genetic algorithm or quantum-behaved particle swarm optimization algorithm and based on the relative entropy of entanglement. We test it on the two-qubit system, and compare it to accurate calculations using the entanglement of formation. The results show that our proposed method is effective and feasible, and has strong adaptability and practicality.