Quantum Feature Mapping for SVM Classification Precision
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Solution Overview
Problem
The use of shift-invariant kernels in support vector machines (SVMs) limits the feature space, resulting in low classification precision and inaccurate classification results.
Innovation Solution
A data classification method and system that utilizes a quantum computer to perform feature mapping on to-be-classified data using a quantum circuit, determining an estimation result based on a boundary vector and quantum states of index information corresponding to the boundary vector, which is then used by a classical computer to improve classification accuracy by not being limited to shift-invariant kernels.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If feature maps corresponding to shift-invariant kernels are used in SVM, then processing efficiency of large data sets is improved, but classification precision deteriorates due to limited feature space
Solution Approach 1:
The patent applies quantum feature maps that map classical data into a high-dimensional quantum feature space, transcending the limitations of traditional shift-invariant kernel feature spaces. This dimensional transformation enables the SVM to access a richer feature representation that improves classification precision while maintaining computational efficiency through quantum parallelism.
Solution Approach 2:
The patent changes the fundamental parameters of the feature mapping process by using quantum mechanical transformations instead of classical kernel functions. By utilizing quantum states and quantum circuit operations, the system achieves both high processing efficiency and improved classification precision, resolving the contradiction between speed and accuracy.
2Measurement precision
If quantum feature maps are used instead of shift-invariant kernels, then classification precision is improved through expanded feature space, but device complexity increases due to quantum computer requirements
Solution Approach 1:
The patent introduces a quantum computer as an intermediary device that handles the complex quantum feature mapping operations, while the classical system continues to manage data preprocessing and post-processing. This intermediary approach allows the system to leverage quantum computational power for feature mapping without requiring complete system redesign, thus improving precision while managing complexity through specialized hardware.
3Device complexity
If quantum computers are used for data classification, then computational complexity is reduced through quantum parallelism, but ease of operation deteriorates due to quantum state management requirements
Solution Approach 1:
The patent segments the classification task into distinct phases: classical data preprocessing, quantum feature mapping and computation, and classical result interpretation. This segmentation allows each component to be optimized independently, reducing overall computational complexity while managing operational complexity through clear separation of concerns and specialized handling at each stage.
Data Source
AI summary
A data classification method and system, and a classifier training method and system are disclosed in the embodiments of the present disclosure, belonging to the field of artificial intelligence (AI), cloud technologies, and quantum technologies. The method includes: providing to-be-classified data to a quantum computer; performing feature mapping on the to-be-classified data by using a quantum circuit to obtain a quantum state of the to-be-classified data; determining an estimation result according to a boundary vector of a classifier, the quantum state of the to-be-classified data, and a quantum state of index information corresponding to the boundary vector; transmitting the estimation result to a classical computer. The quantum state of the index information refers to a superposition of feature maps of training data used by the classifier during training; and determining a classification result corresponding to the to-be-classified data according to the estimation result.