Quantum Information Fusion and Correction with Dempster-Shafer Structure

TL;DR

This paper explores the use of Dempster-Shafer structures in quantum computing, demonstrating how belief functions can be effectively implemented for information fusion and correction, offering advantages over Bayesian methods.

Contribution

It introduces a novel encoding of Dempster-Shafer structures as quantum states and proposes new belief transfer methods, advancing quantum AI uncertainty management.

Findings

Belief functions are more concise than Bayesian approaches in quantum contexts.

Quantum implementation of Dempster-Shafer structures improves information fusion.

Proposed methods enhance uncertainty handling in quantum AI models.

Abstract

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.