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A fuzzy classification system is constructed based on quantum genetic algorithm (QGA) and fuzzy theory. Firstly, fuzzy rules are generated from numerical data for classification problems, in which number axis is fuzzy partitioned with trapezoid method. Second, it uses QGA to select significant fuzzy rules and removes unnecessary rules, so fuzzy rules reach an optimization state. Finally, the feasibility and the validity of this QGA-based approach to fuzzy classification system are verified through the pattern recognition.