Abstract: The present invention provides for a system and a method for improved representation of classical data on quantum systems. Input classical data is received and a feature set is created from the classical data. A functional transformation is performed on the created feature set to reduce high dimensional data associated with the feature set and a low dimensional feature space dataset is generated. A feature space transformation is performed on the low-dimensional feature space dataset to obtain a new feature space dataset with enhanced feature representation of the low-dimensional feature space dataset in a multi-dimensional space. The new feature space dataset results in optimal mapping of the input classical data into a quantum format. The new feature space dataset is sampled and batches of the sampled dataset are selected. The sampled dataset is mapped into an optimized quantum format for loading the sampled dataset into quantum states.

Abstract: The present invention provides for a system and a method for improved representation of classical data on quantum systems. Input classical data is received and a feature set is created from the classical data. A functional transformation is performed on the created feature set to reduce high dimensional data associated with the feature set and a low dimensional feature space dataset is generated. A feature space transformation is performed on the low-dimensional feature space dataset to obtain a new feature space dataset with enhanced feature representation of the low-dimensional feature space dataset in a multi-dimensional space. The new feature space dataset results in optimal mapping of the input classical data into a quantum format. The new feature space dataset is sampled and batches of the sampled dataset are selected. The sampled dataset is mapped into an optimized quantum format for loading the sampled dataset into quantum states.

Abstract: System and method for computing critical data of multiple entities is provided. Elements, from a set of predetermined elements, are assigned to sentences of a first dataset associated with an entity of an industry type. Predetermined elements characterize reputation of entities of various industries. Features determined from sentences of first dataset are categorized into groups. Features of a new dataset of a different entity of said industry type are matched with grouped features and same groups are allotted thereto. Classification rules are applied on matched features. Elements are assigned to sentences of new dataset based on allotted groups, which elements are same as those assigned to first dataset. Reputation scores are generated for said entities by determining positive and negative sentiments from the first and new dataset. Steps of assigning elements and grouping are repeated for datasets associated with entities of different industry types for creating a taxonomy for them.

Abstract: System and method for computing critical data of multiple entities is provided. Elements, from a set of predetermined elements, are assigned to sentences of a first dataset associated with an entity of an industry type. Predetermined elements characterize reputation of entities of various industries. Features determined from sentences of first dataset are categorized into groups. Features of a new dataset of a different entity of said industry type are matched with grouped features and same groups are allotted thereto. Classification rules are applied on matched features. Elements are assigned to sentences of new dataset based on allotted groups, which elements are same as those assigned to first dataset. Reputation scores are generated for said entities by determining positive and negative sentiments from the first and new dataset. Steps of assigning elements and grouping are repeated for datasets associated with entities of different industry types for creating a taxonomy for them.

Abstract: A system and method for generating a hypergraph representative of a comprehensive profile of user is provided. A graphical representation of selected user's transactional behavior is generated. Further, user data is retrieved from external systems for a predetermined time period. A first variable set is derived from the retrieved data and classified into data fields such that the variables across relevant data fields are linkable based on predetermined data category types. Further, new data fields are generated for realizing classification of additional retrieved data. A second variable set from the new data is retrieved and classified into new data fields such that variables across relevant new data fields are linkable based on the predetermined data category types. Two or more graphical representations are generated by linking the variables across the relevant data fields based on the predetermined data category types. Finally, a hypergraph is generated by integrating the graphical representations.