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Hybrid quantum-classical multi-agent decision-making framework based on hierarchical Bayesian networks in the noisy intermediate-scale quantum era |
| Hao Shi(石皓)1, Chenghao Han(韩成豪)1, Peng Wang(王鹏)2, and Ming Zhang(张明)1,† |
1 College of Intelligence Science and Technology, National University of Defense Technology, Changsha 410073, China;
2 College of Science, National University of Defense Technology, Changsha 410073, China |
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Abstract Although quantum Bayesian networks provide a promising paradigm for multi-agent decision-making, their practical application faces two challenges in the noisy intermediate-scale quantum (NISQ) era. Limited qubit resources restrict direct application to large-scale inference tasks. Additionally, no quantum methods are currently available for multi-agent collaborative decision-making. To address these, we propose a hybrid quantum-classical multi-agent decision-making framework based on hierarchical Bayesian networks, comprising two novel methods. The first one is a hybrid quantum-classical inference method based on hierarchical Bayesian networks. It decomposes large-scale hierarchical Bayesian networks into modular subnetworks. The inference for each subnetwork can be performed on NISQ devices, and the intermediate results are converted into classical messages for cross-layer transmission. The second one is a multi-agent decision-making method using the variational quantum eigensolver (VQE) in the influence diagram. This method models the collaborative decision-making with the influence diagram and encodes the expected utility of diverse actions into a Hamiltonian and subsequently determines the intra-group optimal action efficiently. Experimental validation on the IonQ quantum simulator demonstrates that the hierarchical method outperforms the non-hierarchical method at the functional inference level, and the VQE method can obtain the optimal strategy exactly at the collaborative decision-making level. Our research not only extends the application of quantum computing to multi-agent decision-making but also provides a practical solution for the NISQ era.
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Received: 09 April 2025
Revised: 26 June 2025
Accepted manuscript online: 15 July 2025
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PACS:
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03.67.Ac
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(Quantum algorithms, protocols, and simulations)
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03.67.-a
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(Quantum information)
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| Fund: Project supported by the National Natural Science Foundation of China (Grant Nos. 62473371 and 61673389). |
Corresponding Authors:
Ming Zhang
E-mail: zhangming@nudt.edu.cn
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| About author: 2025-120304-250625.pdf |
Cite this article:
Hao Shi(石皓), Chenghao Han(韩成豪), Peng Wang(王鹏), and Ming Zhang(张明) Hybrid quantum-classical multi-agent decision-making framework based on hierarchical Bayesian networks in the noisy intermediate-scale quantum era 2025 Chin. Phys. B 34 120304
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