Reasoning About Quantum Knowledge

M. Dalla Chiara, Roberto Giuntini, Richard J. Greechie

Jincheng Zhang

Knowledge graphs (KGs) are increasingly utilized for representing and reasoning over complex data. Traditional relational reasoning methods within KGs often struggle with scalability and efficiency, particularly when dealing with large and intricate graph structures. This paper proposes a novel approach to relational reasoning in KGs by incorporating quantum-inspired techniques. Specifically, we explore the application of Grover's search algorithm to accelerate the traversal and inference processes. Our core claim is that leveraging quantum-inspired algorithms can significantly improve the performance of querying and reasoning within KGs. We present a framework for adapting Grover's algorithm to KG traversal and inference, outlining the key steps and considerations. The resulting system demonstrates the potential for enhanced efficiency compared to traditional symbolic reasoning, particularly in scenarios with high computational complexity. We provide a detailed mathematical formulation of the underlying concepts and algorithms. The primary contribution of this work lies in demonstrating a viable pathway to harnessing the power of quantum-inspired methods for relational reasoning in knowledge graphs, opening possibilities for more scalable and intelligent knowledge representation and reasoning systems.

Junliang Chen, Xianchao Zhang, Fengsong Sun +1 more

Knowledge reasoning is a critical task in information fusion systems, and its core step is reasoning missing information from existing facts to improve the knowledge graphs. Embedding-based reasoning methods and path-based reasoning methods are two mainstream knowledge reasoning methods. Embedding-based reasoning methods enable fast and direct reasoning but are limited to simple relationships between entities and exhibit poor performance in reasoning complex logical relationships. Path-based reasoning methods perform better in complex reasoning tasks, but suffer from high computational complexity, a large number of model parameters, and low reasoning efficiency. To address the aforementioned issues, this paper introduces a knowledge reasoning model called Quantum-Inspired Reinforcement Learning (QIRL). QIRL leverages quantum reinforcement learning to train a strategy network via a quantum circuit, aiming to generate and optimize reasoning paths. Quantum circuit achieves complex nonlinear operations through limited quantum reasoning paths gate operations, reducing computational complexity. In addition, this article utilizes the quantum entanglement property to encode high-dimensional data, reducing the number of model training parameters. This article evaluates the QIRL method on entity prediction task and proves that the QIRL method can effectively reduce the number of model training parameters.

Paulo Mateus, Amı́lcar Sernadas

Dominique Unruh

Vincent Danos A, Ellie D’Hondt

We prove that quantum key distribution is secure against several types of attacks within the framework of classical knowledge knowledge for quantum systems, a formal model which was developed in [D'Hondt, E. and P. Panangaden, Reasoning about quantum knowledge, in: Proceedings of the 25th Conference on Foundations of Software Technology and Theoretical Computer Science, LNCS 3821, 2005, p. 0544c (to appear), quant-ph/0507176]. In particular we rephrase security as a logical property and use meta-logic reasoning on the finite state machine corresponding to the quantum key distribution protocol. While these security issues have been studied before, it is the logical-based approach that is original here.