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Quantum Algorithm of Dempster Combination Rule

Lipeng Pan, Xiaozhuan Gao, Yong Deng

Published Dec 1, 2020• 1 citation
OpenAlex

Abstract

Dempster combination rule is widely used in many applications such as information fusion and decision making. However, the computational complexity of Dempster combination rule increases exponentially with the increase of frame of discernment. To address this issue, we propose the quantum algorithm of Dempster combination rule based on quantum theory. The algorithm not only realizes most of the functions of Dempster combination rule, but also effectively reduces the computational complexity of Dempster combination rule in future quantum computer. Meanwhile, we carried out a simulation experiment on the quantum cloud platform of IBM, and the experimental results showed that the algorithm is reasonable.

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Dempster combination rule is widely used in many applications such as information fusion and decision making. However, the computational complexity of Dempster combination rule increases exponentially with the increase of frame of discernment. To address this issue, we propose the quantum algorithm of Dempster combination rule based on quantum theory. The algorithm not only realizes most of the functions of Dempster combination rule, but also effectively reduces the computational complexity of Dempster combination rule in future quantum computer. Meanwhile, we carried out a simulation experiment on the quantum cloud platform of IBM, and the experimental results showed that the algorithm is reasonable.
The belief function in Dempster Shafer evidence theory can express more information than the traditional Bayesian distribution. It is widely used in approximate reasoning, decision-making and information fusion. However, its power exponential explosion characteristics leads to the extremely high computational complexity when handling large amounts of elements in classic computers. In order to solve the problem, we encode the basic belief assignment (BBA) into quantum states, which makes each qubit correspond to control an element. Besides the high efficiency, this quantum expression is very conducive to measure the similarity between two BBAs, and the measuring quantum algorithm we come up with has exponential acceleration theoretically compared to the corresponding classical algorithm. In addition, we simulate our quantum version of BBA on Qiskit platform, which ensures the rationality of our algorithm experimentally. We believe our results will shed some light on utilizing the characteristic of quantum computation to handle belief function more conveniently.
This paper introduces a novel quantum algorithm, Quantum Algorithm Based on Information Entropy, designed to efficiently determine data distribution characteristics and optimize machine learning models. The algorithm leverages quantum entanglement and measurement to rapidly assess data distribution features, utilizing information entropy for computation and offering a new measurement method. Traditional computational approaches often exhibit linear complexity, presenting a significant advantage for complex data analysis. This research explores the potential of quantum mechanics to accelerate key computational tasks within machine learning and data science.
Optimal query distribution in information systems plays a crucial role in minimizing delays, balancing workload, and ensuring system stability. Considering multi-factor parameters such as server capacity, network latency, and service rate, the fuzzy logic approach proves effective in handling uncertainty during decision-making. However, an increase in the number of linguistic variables leads to exponential growth of the fuzzy rule base, thereby increasing computational complexity and limiting real-time performance. Traditional optimization methods, including genetic algorithms, are constrained by slow convergence and a tendency to fall into local extrema in large search spaces. This paper proposes a multi-objective quantum optimization approach aimed at reducing the number of fuzzy rules in query distribution processes. By applying Grover's quantum algorithm, the search for optimal rules is significantly accelerated, and the search space is significantly reduced. Experimental results demonstrate reduced response time and improved load balancing. The proposed approach is evaluated as a promising quantum-intelligent mechanism for enhancing load management efficiency in cloud computing environments.
The rapid advancement of artificial intelligence (AI) and quantum computing has introduced new paradigms for solving computationally complex problems beyond the capabilities of classical systems. This paper presents a Quantum Intelligence Platform (QIP), a hybrid quantum-inspired computational framework designed to enhance intelligent decision prediction and optimization in complex environments. The proposed system integrates quantum probabilistic modelling concepts such as superposition and interference with classical machine learning algorithms to improve prediction accuracy and adaptive decision-making. A hybrid architecture combining quantum circuit simulation and classical deep learning modules is developed to enable scalable experimentation without requiring physical quantum hardware. The platform is evaluated on decision prediction and optimization tasks, demonstrating improvements in convergence stability and predictive performance compared to conventional models. Experimental results show that quantum-inspired state representation enhances feature expressiveness and probabilistic reasoning. The proposed framework bridges the gap between theoretical quantum computation and practical intelligent systems, offering a scalable pathway for future quantum-enhanced AI applications.