About Me
Welcome to my homepage! I am an Associate Professor in the School of Electronic Information at Northwestern Polytechnical University.
In 2021.09-2025.06, I got the doctoral degree in Computer Science and Technology under the supervision of Prof. Yong Deng at the Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China.
In 2021.03-2021.08, I joined the Foresight Research Laboratory at the Chinese Academy of Sciences as a research intern graduate student under the supervision of Dr. Guojing Tian, focusing on belief functions and quantum computing.
In 2022.11-2023.10, I joined the Witold Pedrycz’s Lab at the University of Alberta as a visiting student, focusing on belief functions and granular computing.
Privously, I got the bachelor’s degree from the School of Information and Communication Engineering, University of Electronic Science and Technology of China.
In my spare time, I have a strong interest in tennis and fitness. In e-sports, my unique heroes are Rek’sai in League of Legends and Athena in Honor of Kings.
Research Interests
My research interests span the interdisciplinary domains of information fusion, granular computing, quantum computing, uncertainty management. The specific topics are introduced below.
1. Bridges on belief function, possibility and probability
2. Granular computing towards on the belief structure
3. Uncertainty measures in generalized information theory
4. Updating belief on quantum circuits
5. Fractals and chaos phenomenon in uncertain information
I am looking for colleagues who are interested in the above topics to collaborate. The following are some of my research attitudes. If we have the similar attitude, please do not hesitate to contact me!
» Don’t make bad money to drive out good money.
» Don’t do research that deliberately pleases others.
» Resolutely defend original knowledge and respect original authors.
» Don’t shy away from criticism and comments for own research.
» Respect science and admire academics.
» Help colleagues develop without violating the above principles.
Awards and Achievements
[2025] Tennis NWU Open Singles Champion.
[2025] The CSU Youth Support Project for Doctoral Candidates.
[2024] Tennis123 Chengdu 4.0+ Singles Runner-up.
[2024] Tennis123 Chengdu 3.0 Singles Champion.
[2024] The 90th Athena of Shandong.
[2020, 2022, 2023, 2024] National Scholarship for Graduate Students.
[2022] Academic Rookie of University of Electronic Science and Technology of China.
[2021] Academic Young Talents of University of Electronic Science and Technology of China.
[2021] Outstanding Papers of the 10th Chinese Information Fusion Conference (2/210).
[2018] Sichuan Provincial College Tennis Competition Men’s Doubles Runner-up.
Publications
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Unpublished
Qianli Zhou, H. Luo, L. Pan, Y. Deng*, and É. Bossé, “Transferable Belief Model on Quantum Circuits,”IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, Under Review. [ArXiv]
Qianli Zhou, T. Zhan, and Y. Deng*, “Isopignistic Canonical Decomposition via Belief Evolution Network,”IEEE Transactions on Artificial Intelligence, Under Review. [ArXiv]
H. Luo, Qianli Zhou*, L. Pan, Z, Li and Y. Deng*, “Attribute Fusion-based Evidential Classifier on Quantum Circuits,” IEEE Transactions on Pattern Analysis and Machine Intelligence, Under Review. [ArXiv]
Journal articles
2025
Qianli Zhou, and Y. Deng*, “Layer-2 transferable belief model: Manage uncertainty on random permutation sets,” Journal of Electronic Science and Technology, Publishing. [DOI]
TL;DR: We provide a comprehensive approach for handling and modeling uncertainty, capable of representing both quantitative and qualitative information. The advantages of this model are validated through a classifier that leverages attribute fusion to enhance performance and decision-making accuracy
Qianli Zhou, W. Pedrycz, and Y. Deng*, “Order-2 Probabilistic Information Fusion on Random Permutation Set,” IEEE Transactions on Knowledge and Data Engineering, Online Published. [DOI], Supplementary File
TL;DR: We develop an updating method for order-2 probabilistic information and use it to model information fusion of random permutation sets under a random finite set interpretation.
2024
L. Li, Qianli Zhou*, Y. Deng*, and É. Bossé, “Towards an efficient implementation of Dempster–Shafer: alpha-junction fusion rules on quantum circuits,” Quantum Information Processing, Publishing. [DOI]
TL;DR: We show that when using VQLS to implement the matrix fusion method of the belief function, alpha-junction, the matrix operations of the belief function can be implemented faster than the general matrix.
L. Zhu, Qianli Zhou*, Y. Deng*, and W. Pedrycz, “Information fusion in order-2 fuzzy environments: A matrix transformation perspective,” Fuzzy Sets and Systems, vol. 498 pp. 109146. [DOI]
TL;DR: We develop a non-optimized order-2 fuzzy sets fusion method from the perspective of matrix decomposition.
Q. Hu, Qianli Zhou*, Y. Deng*, and K. H. Cheong*, “Evidence generalization-based discounting method: Assigning unreliable information to partial ignorance,” Artificial Intelligence Review, vol. 57 no. 9, pp. 1-27. [DOI]
TL;DR: For unreliable mass function, we provide a more flexible discounting approach based on the generalization of belief functions. Compared with the Shafer’s discounting approach, the reliable status of the sources can be adjusted with less information loss.
Qianli Zhou, Y. Cui, W. Pedrycz, and Y. Deng*, “Conjunctive and disjunctive combination rules in random permutation set theory: A layer-2 belief structure perspective,” Information Fusion, vol. 102, pp. 102–083. [DOI]
TL;DR: Random permutation set is interpreted as a refined belief structure, called a layer-2 belief structure, and the corresponding disjunctive and conjunctive combination rules are extended under the proposed interpretation.
Y. Cai, Qianli Zhou*, and Y. Deng*, “PSO-ECM: Particle swarm optimization-based evidential c-means algorithm,” International Journal of Machine Learning and Cybernetics, vol. 15, pp. 4133–4153. [DOI]
TL;DR: PSO is employed to optimize the initial point selection of the ECM, enhancing the stability of ECM results and more accurately extracting the inherent uncertainty in the unlabeled data.
B. Gao, Qianli Zhou, and Y. Deng*, “HIE-EDT: Hierarchical interval estimation-based evidential decision tree,” Pattern Recognition, vol. 146, pp. 110–040. [DOI]
TL;DR: Constructrions of evidential decision trees is developed based on the hierarchical interval estimation. For evidential data, the more reasonable screening attributes in uncertain environments is achieved.
H. Luo, Qianli Zhou, Z. Li, and Y. Deng*, “Variational quantum linear solver-based combination rules in Dempster–Shafer theory,” Information Fusion, vol. 102, pp. 102–070. [DOI]
TL;DR: The matrix calculus of the belief function is implemented on quantum circuits in the context of a variational quantum linear solver.
L. Zhu, Qianli Zhou, Y. Deng*, and K. H. Cheong*, “Fractal-based basic probability assignment: A transient mass function,” Information Sciences, vol. 652, pp. 119–767. [DOI]
TL;DR: A transient state of the mass function is proposed. By applying probabilistic uncertainty measures to the transient state, evidential uncertainty measures can be realized, which is mathematically proven.
2023
Qianli Zhou, Y. Deng*, and R. R. Yager, “CD-BFT: Canonical decomposition-based belief functions transformation in possibility theory,” IEEE Transactions on Cybernetics, vol. 54, no. 1, pp. 611–623. [DOI]
TL;DR: Through canonical decomposition, a novel transformation between possibility distribution and belief function is proposed. Compared with α-cut, the proposed approach satisfies the combination rule consistency. In addition, the relationship between imprecise possibilistic and probabilstic information is discussed.
Qianli Zhou, W. Pedrycz, Y. Liang, and Y. Deng*, “Information granule-based uncertainty measure of fuzzy evidential distribution,” IEEE Transactions on Fuzzy Systems, vol. 31, no. 12, pp. 4385-4396. [DOI]
TL;DR: The multi-valued mapping of the mass function is interpreted as the construction of information granules, and an uncertainty measure that can be degenerated into the Gini index is developed, and this measure can be extended to fuzzy evidentail information.
Qianli Zhou, Y. Cui, Z. Li, and Y. Deng*, “Marginalization in random permutation set theory: From the cooperative game perspective,” Nonlinear Dynamics, vol. 111, pp. 13125-13141. [DOI]
TL;DR: The relationship between Shapley values and pignistic probability transformation is extended to the ordered structures. Based on the MEOWA, the transformation from random permutation set to its marginal structure developed for the first time.
Qianli Zhou, G. Tian, and Y. Deng*, “BF-QC: Belief functions on quantum circuits,” Expert Systems with Applications, vol. 223, pp. 119–885. [DOI]
TL;DR: The mathematical consistency between power sets and quantum superposition states is leveraged to encode the mass function on a quantum state, enabling accelerated preparation of belief functions. Additionally, the HHL algorithm is used to perform matrix calculus on belief functions within quantum circuits.
Qianli Zhou and Y. Deng*, “Generating sierpinski gasket from matrix calculus in Dempster–Shafer theory,” Chaos, Solitons & Fractals, vol. 166, pp. 112–962. [DOI]
TL;DR: Sierpinski gasket are discovered for the first time in matrix calculus of the belief functions, and an IFS based on matrix calculus is developed.
Z. Wang, Qianli Zhou, and Y. Deng*, “Belief entropy rate: A method to measure the uncertainty of interval-valued stochastic processes,” Applied Intelligence, vol. 53, pp. 17476–17491.[DOI]
TL;DR: The implementation from the interval-valued sequence into a mass function is provided, and the corresponding entropy rate is developed based on the fractal-based belief entropy.
2022
Qianli Zhou, É. Bossé, and Y. Deng*, “Modeling belief propensity degree: Measures of evenness and diversity of belief functions,” IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 53, no. 5, pp. 2851–2862. [DOI]
TL;DR: A novel uncertainty quantification method of mass function- propensity degree - is proposed, which is derived from the fractal dimension of time fractal-based belief entropy, which enriches the uncertainty framework of generalized information theory.
B. Gao, Qianli Zhou, and Y. Deng*, “BIM-AFA: Belief information measure-based attribute fusion approach in improving the quality of uncertain data,” Information Sciences, vol. 608, pp. 950–969. [DOI]
TL;DR: Yager’s information fusion quality screening method is extended to the belief function framework, and a method for enhancing data quality is proposed to improve the representation capability of attributes within the classifier.
Qianli Zhou and Y. Deng*, “NPNT: Non-oscillating process negation transformation of mass functions and a negation-based discounting method in information fusion,” Engineering Applications of Artificial Intelligence, vol. 116, pp. 105–381. [DOI]
TL;DR: From the perspective of cognitive negation, i.e., from known to not unknown, combined with the disjunctive combination rule, a negation approach is proposed. And it is extended to a more efficient discounting apprach than Shafer’s approach.
Qianli Zhou, Y. Huang, and Y. Deng*, “Belief evolution network-based probability transformation and fusion,” Computers & Industrial Engineering, vol. 174, pp. 108–750. [DOI]
TL;DR: The Belief Evolution Network (BEN) is proposed by introducing causality into the Hierarchical Hypothesis Space (HHS). Additionally, a new probability transformation method is introduced, achieving SOTA performance under Bi-Criteria evaluation.
Qianli Zhou, Y. Deng*, and W. Pedrycz, “Information dimension of Galton board,” Fractals, vol. 30, no. 04, pp. 2 250–079. [DOI]
TL;DR: We explore the dimension of Galton board from the perspective of information entropy and generalize a group of Poisson distributions to the similar conclusion.
2021
Qianli Zhou and Y. Deng*, “Fractal-based belief entropy,” Information Sciences, vol. 587, pp. 265–282. [DOI]
TL;DR: By simulating the process of pignisitic probability transformation and inspired by fractal theory, a fractal-based belief entropy is proposed, which corrects the counter-intuitive result of Deng entropy.
Qianli Zhou and Y. Deng*, “Higher order information volume of mass function,” Information Sciences, vol. 586, pp. 501–513. [DOI]
TL;DR: A higher-order form of Deng entropy, referred to as time fractal-based belief entropy, is proposed. It explains the counter-intuitive results of Deng entropy and demonstrates that as the order increases, the results of Deng entropy become progressively more reasonable.
Qianli Zhou and Y. Deng*, “Belief eXtropy: Measure uncertainty from negation,” Communications in Statistics - Theory and Methods, vol. 52, no. 11, pp. 3825–3847. [DOI]
TL;DR: The dual entropy, eXtropy, is interpreted from the negative perspective, and the SU measure is extended into its dual form, i.e., the information volume of the dual of mass function.
2020
Qianli Zhou, H. Mo, and Y. Deng*, “A new divergence measure of pythagorean fuzzy sets based on belief function and its application in medical diagnosis,” Mathematics, vol. 8, no. 1. [DOI]
TL;DR: The Pythagorean fuzzy subset (PFS) is modeled in the belief function framework , and the belief divergence is developed to measure the dissimilarity of PFSs.
Conference proceedings
2024
Qianli Zhou, H. Luo, É. Bossé, and Y. Deng*, “Why combining belief function on quantum circuits?” In 8th International Conference on Belief Functions, pp.161–170. [DOI]
TL;DR: This paper develops combination rules based on Boolean algebra and implements them on quantum circuits, demonstrating the unique acceleration advantages of quantum algorithms on the belief function.
2022
Qianli Zhou, C. Qiang, and Y. Deng*, “Fuzzy fractal: An information entropy view,” in Intelligent Methods Systems and Applications in Computing, Communications and Control: 9th International Conference on Computers Communications and Control (ICCCC), Springer, pp. 250–259. [DOI]
TL;DR: This paper provides the measurement methods of information dimensions of classical fuzzy sets and intuitive fuzzy sets from the perspective of fuzzy entropy.
2021
Qianli Zhou and Y. Deng*, “Handling uncertainty in view of inner product,” in 2021 IEEE International Conference on Unmanned Systems (ICUS), 2021, pp. 305–308. [DOI]
TL;DR: This paper extends the fractal-based belief entropy to the framework of the Gini index and proves its effectiveness in uncertainty measurements for belief functions.