Quantum Decision-Making Algorithms Modeling in the Classical Simulator
the Quantum Software Engineering Visualization for the Educational Process
Abstract
The difference between classical and quantum algorithms (QA) is as follows: the problem solved by QA is coded in the structure of the quantum operators. Input to QA in this case is always the same. Output of QA says which problem coded. In some sense, give a function to QA to analyze and QA returns its property as an answer without quantitative computing. QA studies qualitative properties of the functions. The core of any QA is a set of unitary quantum operators or quantum gates. In practical representation, quantum gate is a unitary matrix with particular structure. The size of this matrix grows exponentially with an increase in the number of inputs, which significantly limits the QA simulation on a classical computer with von Neumann architecture. Quantum search algorithm (QSA) - models apply for the solution of computer science problems as searching in unstructured database, quantum cryptography, engineering tasks, control system design, robotics, smart controllers, etc. Grover’s algorithm is explained in details, along with implementations on a local computer simulator. The presented article describes a practical approach to modeling one of the most famous QA on classical computers, the Grover algorithm.
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