Mphasis has been granted a patent for a system and method that enhances the representation of classical data on quantum systems. The invention involves creating a feature set from classical data, performing transformations to reduce dimensionality, and optimizing the mapping of this data into a quantum format for improved predictive tasks. GlobalData’s report on Mphasis gives a 360-degree view of the company including its patenting strategy. Buy the report here.
Smarter leaders trust GlobalData
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 June 2024 was 27%. Grant share is based on the ratio of number of grants to total number of patents.
Improved representation of classical data on quantum systems
The patent US11989162B2 describes a sophisticated system designed to enhance the representation of classical data within quantum systems. The system comprises several key components, including a memory for storing program instructions, a processor for executing these instructions, and a feature definition engine that processes input classical data to create a feature set. This feature set undergoes a functional transformation to reduce high-dimensional data into a low-dimensional feature space dataset, ensuring no loss of information. The system further includes a feature space transformation engine that optimizes the representation of this dataset in a multi-dimensional space, facilitating the effective mapping of classical data into a quantum format. Additionally, a batch preparation and selection engine samples the new feature space dataset, while a quantum prediction engine converts these samples into an optimized quantum format for loading into quantum states.
The claims also detail specific functionalities of the feature definition engine, which can process various types of classical data, such as images and text. For image data, the engine converts images into pixel arrays, applies transformations, and creates a feature set based on pixel analysis. For text data, it employs word embedding techniques to convert text into numerical values, identifying relationships through machine learning models. The system incorporates mechanisms for de-noising data, dimensionality reduction using advanced methods, and continuous evaluation of the feature space to refine the dataset for predictive tasks. The quantum prediction engine is designed to map the sampled dataset into quantum states using various quantum parameters and techniques, ensuring efficient quantum-assisted machine learning operations. Overall, the patent outlines a comprehensive method and system for leveraging quantum computing to improve data representation and predictive capabilities.
To know more about GlobalData’s detailed insights on Mphasis, buy the report here.
Data Insights
From
The gold standard of business intelligence.
Blending expert knowledge with cutting-edge technology, GlobalData’s unrivalled proprietary data will enable you to decode what’s happening in your market. You can make better informed decisions and gain a future-proof advantage over your competitors.
GlobalData, the leading provider of industry intelligence, provided the underlying data, research, and analysis used to produce this article.
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.
