A novel quantum-inspired evolutionary computation-based quantum circuit synthesis for various universal gate libraries

Chou, Yao-Hsin; Kuo, Shu-Yu; Jiang, Yu-Chi; Wu, Ching-Hsuan; Shen, Jyun-Yi; Hua, Cheng-Yen; Huang, Po-Yuan; Lai, Yun-Ting; Tong, Yong Feng; Chang, Ming-He

doi:10.1145/3520304.3533956

Cited by 6 publications

(1 citation statement)

References 25 publications

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“…In [17][18][19][20][21][22], quantum-inspired metaheuristics are utilized for job-scheduling problems, and some examples of network applications of quantum-inspired metaheuristics are found in [23][24][25][26]. In [27], a quantum-inspired metaheuristic is used for quantum circuit synthesis. Other applications are feature selection [28,29], fuzzy c-means clustering [30], stock market portfolio optimization [31], flight control optimization [32,33], antenna positioning problems [34], airport gate allocation [35], and multi-objective optimization [36].…”

Section: Introductionmentioning

confidence: 99%

A Modified Quantum-Inspired Genetic Algorithm Using Lengthening Chromosome Size and an Adaptive Look-Up Table to Avoid Local Optima

Hakemi,

Houshmand,

Hosseini

et al. 2023

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The quantum-inspired genetic algorithm (QGA), which combines quantum mechanics concepts and GA to enhance search capability, has been popular and provides an efficient search mechanism. This paper proposes a modified QGA, called dynamic QGA (DQGA). The proposed algorithm utilizes a lengthening chromosome strategy for a balanced and smooth transition between exploration and exploitation phases to avoid local optima and premature convergence. 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, 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 and its superiority for solving multimodal benchmark functions and real-world constrained engineering problems.

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