Quantum Genetic Algorithm with Dynamic Encoding Scheme

Kim, Jun Suk; Kim, Man-Je; Ahn, Chang Wook

doi:10.15388/26-INFOR639

Informatica

Quantum Genetic Algorithm with Dynamic Encoding Scheme

Jun Suk Kim Man-Je Kim Chang Wook Ahn

https://doi.org/10.15388/26-INFOR639

Pub. online: 6 August 2026 Type: Research Article

Open Access

Received
1 January 2026

Accepted
1 July 2026

Published
6 August 2026

Abstract

The field of Quantum Genetic Algorithm (QGA) has seen extensive efforts to develop reasonable algorithmic designs mainly due to its potential as well as the intrinsic difficulty to reproduce the necessary processes with the limited amount of quantum resources available, although it still remains one of rather theoretical concepts of approach to achieving quantum optimization. In this paper, we propose a dynamic encoding scheme that combines quantum adaptive search with iterative approximation of the search region. The method reuses the same quantum index register while updating the classical coordinate mapping associated with its basis states, thereby increasing local coordinate resolution without globally refining the entire continuous domain. Through noiseless statevector simulations on benchmark functions, we compare the proposed method with selected QGA variants in terms of final optimization accuracy and simulated qubit usage. The results show improved optimization performance under similar maximum qubit constraints, establishing a simulation-level resource advantage.

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Biographies

Kim Jun Suk

junsuk89@gmail.com

J.S. Kim received his bachelor’s degree in Physics from University of Illinois in Urbana-Champaign, USA, in 2016 and received his master’s and doctoral degrees in quantum computing from Gwangju Institute of Science and Technology (GIST), Republic of Korea, in 2019 and 2025, respectively. He is currently working as a post-doctoral researcher in the Department of AI and the Institute for AI in GIST. His main topics of research are quantum heuristic optimization, quantum reinforcement learning, and quantum AI.

Kim Man-Je

jaykim0104@jnu.ac.kr

M.-J. Kim is an associate professor in the Department of AI at Chonnam National University, Republic of Korea. He received the PhD degree from the Artificial Intelligence Graduate School, Gwangju Institute of Science and Technology (GIST), Republic of Korea in 2023. His research interests span game artificial intelligence, reinforcement learning, human-computer interaction, and machine learning. Kim is recognized for his work on evolutionary reinforcement learning algorithms and AI systems that integrate human-centric decision-making frameworks.

Ahn Chang Wook

cwan@gist.ac.kr

C.W. Ahn is a professor in the Department of AI at Gwangju Institute of Science and Technology (GIST), Republic of Korea. He received a PhD degree from the Department of Information and Communications at GIST in 2005. From 2005 to 2007, he worked in Samsung Advanced Institute of Technology, Korea. From 2007 to 2008, he was a research professor at GIST. From 2008 to 2016, he was an assistant/associate professor at the Department of Computer Engineering, Sungkyunkwan University (SKKU), Republic of Korea. His research interests include genetic algorithms/programming, multi-objective optimization, neural networks, and quantum machine learning.

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Quantum Algorithm optimization Genetic Algorithm

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