Quantum Genetic Algorithm Source Code Implementation
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In this article, we examine the source code implementation of the Quantum Genetic Algorithm (QGA) to deepen understanding of this advanced computational technique. The QGA represents a sophisticated optimization approach that integrates principles from quantum computing with genetic algorithms, leveraging quantum behavior simulation to accelerate optimization processes. The algorithm's source code comprises critical components including quantum bit (qubit) representations, quantum rotation gate operations, population initialization functions, and fitness evaluation modules. Through comprehensive analysis and modification of these code components - such as adjusting rotation angles in quantum gates or refining selection mechanisms - researchers can enhance the algorithm's performance for broader applications across various domains including machine learning, financial modeling, and complex system optimization.
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