Enhanced Multi-Objective Quantum-Inspired Computing for Practical Portfolio Optimization

Yao Hsin Chou; Yong Feng Tong; Shu Yu Kuo; Yu Chi Jiang; Sy Yen Kuo

doi:10.1109/NANO63165.2025.11113712

Enhanced Multi-Objective Quantum-Inspired Computing for Practical Portfolio Optimization

Yao Hsin Chou^*
, Yong Feng Tong^*
, Shu Yu Kuo
, Yu Chi Jiang
, Sy Yen Kuo

^*Corresponding author for this work

Department of Computer Science and Information Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

Quantum-inspired optimization (QIO) has gained attention for leveraging both quantum and classical advantages to enhance search efficiency in solving complex optimization problems on classical computers. While most QIO approaches have shown strong potential in addressing single-objective optimization, real-world problems often involve conflicting objectives, making them inherently multi-objective and challenging to solve. Unlike single-objective optimization, which seeks a single optimal solution, multi-objective optimization aims to obtain a set of Pareto-optimal solutions. This study extends QIO to multi-objective optimization (MoQIO) by incorporating quantum-inspired characteristics and provides an in-depth analysis of its performance. It utilizes quantum superposition and Q-gates to enhance convergence efficiency toward the Pareto front, while leveraging entanglement properties to expand solution diversity. Our results demonstrate that the proposed MoQIO effectively generates a diverse set of optimal portfolio solutions.

Original language	English
Title of host publication	25th IEEE International Conference on Nanotechnology, NANO 2025
Editors	Francesca Urban, Aniello Pelella, Antonio Di Bartolomeo
Publisher	IEEE Computer Society
Pages	470-474
Number of pages	5
ISBN (Electronic)	9798331512712
DOIs	https://doi.org/10.1109/NANO63165.2025.11113712
State	Published - 2025
Event	25th IEEE International Conference on Nanotechnology, NANO 2025 - Washington, United States Duration: 13 07 2025 → 16 07 2025

Publication series

Name	Proceedings of the IEEE Conference on Nanotechnology
ISSN (Print)	1944-9399
ISSN (Electronic)	1944-9380

Conference

Conference	25th IEEE International Conference on Nanotechnology, NANO 2025
Country/Territory	United States
City	Washington
Period	13/07/25 → 16/07/25

Bibliographical note

Publisher Copyright:
© 2025 IEEE.

Keywords

Multi-objective Quantum-inspired Optimization
Quantum-inspired Computing
Real-World Applications

Access to Document

10.1109/NANO63165.2025.11113712

Cite this

Chou, Y. H., Tong, Y. F., Kuo, S. Y., Jiang, Y. C., & Kuo, S. Y. (2025). Enhanced Multi-Objective Quantum-Inspired Computing for Practical Portfolio Optimization. In F. Urban, A. Pelella, & A. Di Bartolomeo (Eds.), 25th IEEE International Conference on Nanotechnology, NANO 2025 (pp. 470-474). (Proceedings of the IEEE Conference on Nanotechnology). IEEE Computer Society. https://doi.org/10.1109/NANO63165.2025.11113712

Chou, Yao Hsin ; Tong, Yong Feng ; Kuo, Shu Yu et al. / Enhanced Multi-Objective Quantum-Inspired Computing for Practical Portfolio Optimization. 25th IEEE International Conference on Nanotechnology, NANO 2025. editor / Francesca Urban ; Aniello Pelella ; Antonio Di Bartolomeo. IEEE Computer Society, 2025. pp. 470-474 (Proceedings of the IEEE Conference on Nanotechnology).

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title = "Enhanced Multi-Objective Quantum-Inspired Computing for Practical Portfolio Optimization",

abstract = "Quantum-inspired optimization (QIO) has gained attention for leveraging both quantum and classical advantages to enhance search efficiency in solving complex optimization problems on classical computers. While most QIO approaches have shown strong potential in addressing single-objective optimization, real-world problems often involve conflicting objectives, making them inherently multi-objective and challenging to solve. Unlike single-objective optimization, which seeks a single optimal solution, multi-objective optimization aims to obtain a set of Pareto-optimal solutions. This study extends QIO to multi-objective optimization (MoQIO) by incorporating quantum-inspired characteristics and provides an in-depth analysis of its performance. It utilizes quantum superposition and Q-gates to enhance convergence efficiency toward the Pareto front, while leveraging entanglement properties to expand solution diversity. Our results demonstrate that the proposed MoQIO effectively generates a diverse set of optimal portfolio solutions.",

keywords = "Multi-objective Quantum-inspired Optimization, Quantum-inspired Computing, Real-World Applications",

author = "Chou, \{Yao Hsin\} and Tong, \{Yong Feng\} and Kuo, \{Shu Yu\} and Jiang, \{Yu Chi\} and Kuo, \{Sy Yen\}",

note = "Publisher Copyright: {\textcopyright} 2025 IEEE.; 25th IEEE International Conference on Nanotechnology, NANO 2025 ; Conference date: 13-07-2025 Through 16-07-2025",

year = "2025",

doi = "10.1109/NANO63165.2025.11113712",

language = "英语",

series = "Proceedings of the IEEE Conference on Nanotechnology",

publisher = "IEEE Computer Society",

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Chou, YH, Tong, YF, Kuo, SY, Jiang, YC & Kuo, SY 2025, Enhanced Multi-Objective Quantum-Inspired Computing for Practical Portfolio Optimization. in F Urban, A Pelella & A Di Bartolomeo (eds), 25th IEEE International Conference on Nanotechnology, NANO 2025. Proceedings of the IEEE Conference on Nanotechnology, IEEE Computer Society, pp. 470-474, 25th IEEE International Conference on Nanotechnology, NANO 2025, Washington, United States, 13/07/25. https://doi.org/10.1109/NANO63165.2025.11113712

Enhanced Multi-Objective Quantum-Inspired Computing for Practical Portfolio Optimization. / Chou, Yao Hsin; Tong, Yong Feng; Kuo, Shu Yu et al.
25th IEEE International Conference on Nanotechnology, NANO 2025. ed. / Francesca Urban; Aniello Pelella; Antonio Di Bartolomeo. IEEE Computer Society, 2025. p. 470-474 (Proceedings of the IEEE Conference on Nanotechnology).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Kuo, Sy Yen

PY - 2025

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N2 - Quantum-inspired optimization (QIO) has gained attention for leveraging both quantum and classical advantages to enhance search efficiency in solving complex optimization problems on classical computers. While most QIO approaches have shown strong potential in addressing single-objective optimization, real-world problems often involve conflicting objectives, making them inherently multi-objective and challenging to solve. Unlike single-objective optimization, which seeks a single optimal solution, multi-objective optimization aims to obtain a set of Pareto-optimal solutions. This study extends QIO to multi-objective optimization (MoQIO) by incorporating quantum-inspired characteristics and provides an in-depth analysis of its performance. It utilizes quantum superposition and Q-gates to enhance convergence efficiency toward the Pareto front, while leveraging entanglement properties to expand solution diversity. Our results demonstrate that the proposed MoQIO effectively generates a diverse set of optimal portfolio solutions.

AB - Quantum-inspired optimization (QIO) has gained attention for leveraging both quantum and classical advantages to enhance search efficiency in solving complex optimization problems on classical computers. While most QIO approaches have shown strong potential in addressing single-objective optimization, real-world problems often involve conflicting objectives, making them inherently multi-objective and challenging to solve. Unlike single-objective optimization, which seeks a single optimal solution, multi-objective optimization aims to obtain a set of Pareto-optimal solutions. This study extends QIO to multi-objective optimization (MoQIO) by incorporating quantum-inspired characteristics and provides an in-depth analysis of its performance. It utilizes quantum superposition and Q-gates to enhance convergence efficiency toward the Pareto front, while leveraging entanglement properties to expand solution diversity. Our results demonstrate that the proposed MoQIO effectively generates a diverse set of optimal portfolio solutions.

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Chou YH, Tong YF, Kuo SY, Jiang YC, Kuo SY. Enhanced Multi-Objective Quantum-Inspired Computing for Practical Portfolio Optimization. In Urban F, Pelella A, Di Bartolomeo A, editors, 25th IEEE International Conference on Nanotechnology, NANO 2025. IEEE Computer Society. 2025. p. 470-474. (Proceedings of the IEEE Conference on Nanotechnology). doi: 10.1109/NANO63165.2025.11113712

Enhanced Multi-Objective Quantum-Inspired Computing for Practical Portfolio Optimization

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Keywords

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