MphasiS has patented a system and method for enhancing classical data representation on quantum systems. The technology involves creating a feature set, reducing high-dimensional data, and optimizing mapping into a quantum format. The system includes engines for feature definition, space transformation, batch selection, quantum prediction, and continuous evaluation for refinement. GlobalData’s report on MphasiS gives a 360-degree view of the company including its patenting strategy. Buy the report here.
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According to GlobalData’s company profile on MphasiS, AI assisted coding platforms was a key innovation area identified from patents. MphasiS's grant share as of May 2024 was 24%. Grant share is based on the ratio of number of grants to total number of patents.
Improved representation of classical data on quantum systems
A recently granted patent (Publication Number: US11989162B2) discloses a system for enhancing the representation of classical data on quantum systems. The system includes a feature definition engine, a feature space transformation engine, a batch preparation and selection engine, and a quantum prediction engine. The feature definition engine processes input classical data, such as images or text, to create a feature set for analysis. The feature space transformation engine reduces high-dimensional data associated with the feature set and generates a low-dimensional feature space dataset with enhanced feature representation. The batch preparation and selection engine optimally samples the dataset, while the quantum prediction engine maps the sampled data into an optimized quantum format for efficient predictive tasks. The system continuously evaluates performance and provides feedback for refining the feature space dataset iteratively.
The patent also describes a method and a computer program product for implementing the system. The method involves receiving input classical data, performing functional and feature space transformations, sampling the dataset, mapping the sampled data into quantum states, and evaluating system performance for efficient quantum predictive tasks. The computer program product includes instructions for executing the method on a processor. Overall, the system aims to optimize the representation of classical data on quantum systems by leveraging advanced techniques such as feature engineering, dimensionality reduction, and quantum state preparation. The continuous feedback loop ensures the refinement and redefinition of the feature space dataset to enhance predictive capabilities in a quantum computing environment.
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GlobalData Patent Analytics tracks bibliographic data, legal events data, point in time patent ownerships, and backward and forward citations from global patenting offices. Textual analysis and official patent classifications are used to group patents into key thematic areas and link them to specific companies across the world’s largest industries.
