BF-QC: Belief Functions on Quantum Circuits
This work addresses a computational bottleneck in DST for AI applications, potentially enabling broader practical use in scenarios where classical methods are limited by complexity.
The paper tackles the exponential computation complexity of Dempster-Shafer Theory (DST) by encoding Basic Belief Assignments (BBA) into quantum superposition states and implementing operations on quantum circuits, resulting in decreased complexity for matrix evolution and enabling quantum belief functions, similarity measurements, evidence combination, and probability transformation.
Dempster-Shafer Theory (DST) of belief function is a basic theory of artificial intelligence, which can represent the underlying knowledge more reasonably than Probability Theory (ProbT). Because of the computation complexity exploding exponentially with the increasing number of elements, the practical application scenarios of DST are limited. In this paper, we encode Basic Belief Assignments (BBA) into quantum superposition states and propose the implementation and operation methods of BBA on quantum circuits. We decrease the computation complexity of the matrix evolution on BBA (MEoB) on quantum circuits. Based on the MEoB, we realize the quantum belief functions' implementation, the similarity measurements of BBAs, evidence Combination Rules (CR), and probability transformation on quantum circuits.