Hybrid Quantum Feature Selection for Non-Linear ML Dependencies
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Solution Overview
Problem
Current machine learning systems face inefficiencies due to the processing of large volumes of data, including duplicate and highly correlated data, which wastes resources and time without significantly improving model accuracy, and lack effective methods to detect non-linear data dependencies.
Innovation Solution
A hybrid quantum computing approach using a quantum optimizer and classical computer to identify and remove non-linearly correlated data through quantum annealing algorithms, reducing feature sets while maintaining accuracy levels set by the user.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If more data is input into the machine learning model to improve robustness and developedness, then the model accuracy is improved, but the resources and time required to create and enhance the model increase significantly
Solution Approach 1:
The patent extracts and removes duplicate and highly correlated data from the input dataset before feeding it to the machine learning model. By identifying and eliminating redundant features through correlation analysis, the system reduces the amount of useful data that needs to be processed, thereby decreasing the time and resources required for model training while maintaining model accuracy.
Solution Approach 2:
The patent changes the parameter of data dimensionality by reducing the number of features through correlation analysis. By transforming the dataset to remove redundant dimensions, the system optimizes the balance between data quantity and processing requirements, enabling faster model training without sacrificing accuracy.
2Reliability
If more granularly the system reviews the inputted data to improve model developedness, then the model robustness is improved, but the resources and time required to create and enhance the model increase significantly
Solution Approach 1:
The patent extracts and eliminates redundant features by identifying highly correlated data pairs. By removing these redundant dimensions, the system reduces the computational energy required for processing while maintaining the essential information needed for model robustness.
Solution Approach 2:
The patent optimizes the data representation by changing the feature space through correlation-based dimensionality reduction. This transformation maintains the informative content while reducing the computational burden on resources.
3Quantity of substance
If duplicate data or highly correlated data is processed by the machine learning model, then the data volume is maintained, but resources and time are wasted without significant learning improvement
Solution Approach 1:
The patent extracts and removes duplicate and highly correlated data from the dataset. By identifying redundant features through correlation analysis and eliminating them, the system reduces the amount of useless data that wastes computational resources, while retaining only the essential informative data.
Solution Approach 2:
The patent discards redundant and duplicate data while recovering and retaining only the essential informative features. This selective approach ensures that computational resources are not wasted on redundant information while preserving the core data needed for model training.
Data Source
AI summary
Methods for detecting non-linear data dependencies in machine learning using hybrid quantum computing. Methods include receiving a selection of an accuracy metric. Methods include receiving a data set comprising a plurality of data elements for processing by a machine learning model operating on a machine learning system. Methods include identifying a plurality of data elements within each data set. Methods include identifying one or more features for each data element. Methods include determining a total number of features for the data set. Methods include reducing, by a quantum annealing method, based on the accuracy metric, the total number of features to a reduced number of features. Methods include inputting the reduced number of features into the machine learning model. Methods include outputting a result from the machine learning model.