Bayesian network inference remains a computationally intensive task, particularly for large and complex networks. Traditional methods often rely on exhaustive search or approximate techniques that can be slow to converge and may not fully explore the inference space. This paper proposes a novel approach, Quantum-Inspired Probabilistic Resonance (QIPR), which leverages principles from quantum mechanics, specifically superposition and interference, to dramatically enhance Bayesian network inference. The core idea is to introduce a 'resonance' operator that simultaneously evaluates multiple potential belief assignments, akin to quantum interference, guided by the network's structure and prior knowledge. This resonance operator effectively amplifies relevant pathways, accelerating convergence and improving the exploration of the inference space. We detail the mathematical formulation of the QIPR engine, including the probability amplitude calculations and the resonance operator's influence. The methodology offers a potential paradigm shift in Bayesian network inference, offering enhanced efficiency and accuracy in tackling complex probabilistic models. The presented framework provides a robust and adaptable method for Bayesian network inference, addressing limitations of conventional approaches.

Sep 5, 20260 citations

Quantum approaches for inference and decision-making in quantum multi-agent frameworks

Hao Jie Shi, Ming Zhang, Haoqiang Chen +2 more

Abstract In multi-agent systems, Bayesian networks are pivotal for inference and decision-making under uncertainty, yet they face significant challenges, such as high computational complexity and slow decision speed. Quantum computing, leveraging superposition and entanglement, offers potential advantages for solving certain complex problems. Therefore, exploring Bayesian networks within the quantum multi-agent framework promises enhanced inference and decision-making capabilities. While quantum Bayesian networks for inference have been extensively studied, quantum dynamic Bayesian networks for inference and quantum decision networks for decision-making remain underexplored. In the noisy intermediate-scale quantum (NISQ) era, quantum computers struggle to process the long-term temporal structures of dynamic Bayesian networks due to limited resources. To address this, we propose a recursive quantum-classical hybrid Bayesian network inference method, which decomposes dynamic Bayesian networks into smaller subnetworks along the time series. Each subnetwork is represented by a reduced-scale quantum circuit, and the forward and backward operators are computed recursively, enabling efficient filtering and smoothing inference. In addition, we propose an optimal decision-making method based on quantum decision networks, which maps utility values to quantum probabilities and identifies the optimal action by determining the quantum state corresponding to the maximum expected probability. We validate the effectiveness of these algorithms using the IonQ quantum simulator and compare their performance with classical methods. The results demonstrate that our proposed quantum algorithms can effectively perform inference and decision-making tasks well on NISQ devices. The proposed methods provide a foundation for collaborative inference and decision-making within the quantum multi-agent framework.

The European Physical Journal Special TopicsSep 6, 20254 citations