Quantum Information Fusion and Correction with Dempster-Shafer Structure
This work introduces a novel perspective on information representation for handling uncertainty in quantum AI models, potentially benefiting quantum computing applications.
The paper tackles the limitations of Dempster-Shafer structure in classical settings by implementing information fusion and correction on quantum circuits, demonstrating that belief functions provide a more concise and effective alternative to Bayesian approaches within quantum computing.
Dempster-Shafer structure is effective in classical settings for connecting set-valued hypotheses and representing structured ignorance, yet its practical use is limited by combination growth over focal sets and high conflict management. We observe a mathematical consistency between Dempster-Shafer structure and quantum superposition: elements of the power set form an orthogonal basis, and a basic probability assignment can be encoded as a normalized quantum state whose amplitudes respect mass value constraints. In this paper, we implement the information fusion and correction with Dempster-Shafer structure on quantum circuits, demonstrating that belief functions provide a more concise and effective alternative to Bayesian approaches within the quantum computing framework.Furthermore, by leveraging the unique characteristics of quantum computing, we propose several novel approaches for belief transfer. More broadly, this paper introduces a novel perspective on basic information representation in quantum AI models, proposing that belief functions are better suited than Bayesian approaches for handling uncertainty in quantum circuits.