Abstract: A method of performing an inference task on a knowledge graph comprising semantic triples of entities, wherein entity types are subject, object and predicate, and wherein each semantic triple comprises one of each entity type, using a quantum computing device, wherein a first entity of a first type and a second entity of a second type are given and the inference task is to infer a third entity of the third type. By performing specific steps and choosing values according to specific prescriptions, an efficient and resource-saving method is developed that utilizes the power of quantum computing systems for inference tasks on large knowledge graphs. An advantageous value for a cutoff threshold for a cutoff based on singular values of a singular value tensor decomposition is prescribed, and a sequence of steps is developed in which only the squares of the singular values are of consequence and their signs are not.

Abstract: A computer-implemented method and system for assigning at least one query triplet to at least one respective class. The at least one respective class is true or false. The method includes the steps of providing the at least one query triplet and a knowledge graph with a plurality of triples and extracting at least one affirmative argument using reinforcement learning on the basis of the at least one query triplet and the knowledge graph. The at least one affirmative argument indicates that the at least one query triplet is true. The method further includes extracting at least one opposing argument using reinforcement learning on the basis of the at least one query triplet and the knowledge graph. The at least one opposing argument indicates that the at least one query triplet is false. The method further includes assigning the at least one query triplet to the at least one respective class using supervised machine learning depending on the at least two arguments.

Abstract: Various examples generally relate to knowledge graphs including entities and links associated with semantic triples including subject-predicate-object. Various examples specifically relate to quantum-machine learning of knowledge graphs. Further examples relate to a quantum-machine-assisted inference on knowledge graphs.

Abstract: A method of performing an inference task on a knowledge graph comprising semantic triples of entities, wherein entity types are subject, object and predicate, and wherein each semantic triple comprises one of each entity type, using a quantum computing device, wherein a first entity of a first type and a second entity of a second type are given and the inference task is to infer a third entity of the third type. By performing specific steps and choosing values according to specific prescriptions, an efficient and resource-saving method is developed that utilizes the power of quantum computing systems for inference tasks on large knowledge graphs. An advantageous value for a cutoff threshold for a cutoff based on singular values of a singular value tensor decomposition is prescribed, and a sequence of steps is developed in which only the squares of the singular values are of consequence and their signs are not.

Abstract: Various examples generally relate to knowledge graphs including entities and links associated with semantic triples including subject-predicate-object. Various examples specifically relate to quantum-machine learning of knowledge graphs. Further examples relate to a quantum-machine-assisted inference on knowledge graphs.