This paper proposes a Dynamic Quantum-inspired Genetic Algorithm (DQGA), which combines the quantum mechanics concepts with metaheuristics principles to enhance the search capability of the genetic algorithm. The proposed algorithm also introduces the lengthening chromosome strategy for a balanced and smooth transition between exploration and exploitation phases. Apart from that, a novel adaptive look-up table for rotation gates is presented to boost the algorithm's optimization abilities. To evaluate the effectiveness of these ideas, the DQGA is tested by various mathematical benchmark functions as well as real-world constrained engineering problems against several well-known and state-of-the-art algorithms. The obtained results indicate the merits of the proposed algorithm.