Article: Quantum computing-driven portfolio optimisation framework for the intelligent economy Journal: International Journal of Information and Communication Technology (IJICT) 2026 Vol.27 No.98 pp.94 - 114 Abstract: Portfolio optimisation in intelligent economic systems faces the challenge of portfolio explosion caused by expanding asset scales, making it difficult for classical solvers to obtain high-quality solutions within a limited timeframe. This paper proposes a hybrid quantum-classical optimisation framework that combines the global search capabilities of quantum annealing with the constraint expression capabilities of variational quantum algorithms, while reducing quantum resource requirements through asset graph partitioning. In experiments covering 20 to 120 real-market assets, the hybrid framework achieved an area under the curve of 0.716 for a portfolio of 120 assets, representing an approximately 29% improvement over classical genetic algorithms; it maintained an area under the curve of 0.921 even under a noise level of 10<SUP align="right"><SMALL>-3</SMALL></SUP>, demonstrating greater robustness than pure quantum annealing methods. The research confirms that the hybrid paradigm is a viable approach for solving large-scale combinatorial optimisation problems on current noisy quantum devices. Inderscience Publishers - linking academia, business and industry through research

Title: Quantum computing-driven portfolio optimisation framework for the intelligent economy

Authors: Leihua Ai

Addresses: Ganxi Vocational College of Science and Technology, Xinyu, 338025, China

Abstract: Portfolio optimisation in intelligent economic systems faces the challenge of portfolio explosion caused by expanding asset scales, making it difficult for classical solvers to obtain high-quality solutions within a limited timeframe. This paper proposes a hybrid quantum-classical optimisation framework that combines the global search capabilities of quantum annealing with the constraint expression capabilities of variational quantum algorithms, while reducing quantum resource requirements through asset graph partitioning. In experiments covering 20 to 120 real-market assets, the hybrid framework achieved an area under the curve of 0.716 for a portfolio of 120 assets, representing an approximately 29% improvement over classical genetic algorithms; it maintained an area under the curve of 0.921 even under a noise level of 10^-3, demonstrating greater robustness than pure quantum annealing methods. The research confirms that the hybrid paradigm is a viable approach for solving large-scale combinatorial optimisation problems on current noisy quantum devices.

Keywords: quantum computing; portfolio optimisation; smart economy; hybrid quantum framework; asset selection.

DOI: 10.1504/IJICT.2026.156257

International Journal of Information and Communication Technology, 2026 Vol.27 No.98, pp.94 - 114

Received: 13 May 2026
Accepted: 19 Jun 2026
Published online: 08 Sep 2026 *

Title: Quantum computing-driven portfolio optimisation framework for the intelligent economy

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