Natural knowledge

Henry J. Folse

Rafael Pass

Abstract. We revisit the definitions of zero-knowledge in the Common Reference String (CRS) model and the Random Oracle (RO) model. We argue that even though these definitions syntactically mimic the standard zero-knowledge definition, they loose some of its spirit. In particular, we show that there exist a specific natural security property that is not captured by these definitions. This is the property of deniability. We formally define the notion of deniable zero-knowledge in these models and investigate the possibility of achieving it. Our results are different for the two models: – Concerning the CRS model, we rule out the possibility of achieving deniable zero-knowledge protocols in “natural ” settings where such protocols cannot already be achieved in plain model. – In the RO model, on the other hand, we construct an efficient 2-round deniable zero-knowledge argument of knowledge, that preserves both the zero-knowledge property and the proof of knowledge property under concurrent executions (concurrent zero-knowledge and concurrent proof-of knowledge). 1

David Favrholdt

Pulak Ranjan Giri, Mori Kurokawa, Kazuhiro Saito

Knowledge graph is a collection of facts, known as triples(head, relation, tail), which are represented in form of a network, where nodes are entities and edges are relations among the respective head and tail entities. Embedding of knowledge graph for facilitating downstream tasks such as knowledge graph completion, link prediction, recommendation, has been a major area of research recently in classical machine learning. Because the size of knowledge graphs are becoming larger, one of the natural choices is to exploit quantum computing for knowledge graph embedding. Recently, a hybrid quantum classical model for knowledge graph embedding has been studied in which a variational quantum circuit is trained. One of the important aspects in knowledge graph embedding is the sampling of negative triples, which plays a crucial role in efficient training of the model. In classical machine learning various negative sampling strategies have been studied. In quantum knowledge graph embedding model, although we can use these strategies in principle, it is natural to ask if we can exploit quantum advantage in negative sampling. In this article we study such a negative sampling strategy, which exploits quantum superposition, and evaluate the model's performance with a knowledge graph database.

Henry Pierce Stapp

Igor Bessmertny, A.V. Vasiljev, Ю А Королёва +2 more

Creation of intelligent systems in the paradigm of logical decision-making requires formalization of knowledge in the form of ontologies, production models, etc.However, a rigorous formalization of knowledge is far from always possible due to the incompleteness, inaccuracy, and inconsistency of the data.In this regard, recently, the attention of researchers has shifted to extracting knowledge from natural language texts.Particularly noteworthy is the recently developing approach based on the use of quantum formalism to the objects of the macrocosm, which allows one to consider the uncertainty and inaccuracy inherent in the natural language.Numerous experiments conducted over the past 30 years demonstrate that the mathematical apparatus developed for modeling elementary particles also satisfactorily describes the behavior of people, which cannot be described by the mathematical apparatus of classical logic and probability theory.A review of the methods of processing natural language texts by means of quantum mathematics is presented.The methods are designed to eliminate the shortcomings of existing methods and means of information retrieval.