Quantum Vision System for Image Classification
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Solution Overview
Problem
Existing image classification systems face challenges with high data processing times due to large image sizes and the difficulty in identifying features with low contrast, especially in high-dimensional data sets.
Innovation Solution
A quantum vision system using noisy intermediate-scale quantum (NISQ) hardware and quantum machine learning techniques, combined with contrast enhancement and dimensionality reduction through methods like contrast stretching, histogram equalization, and principal component analysis, to classify images efficiently and accurately.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If classical deep convolutional neural networks are used for image classification, then classification capability is achieved, but processing time increases significantly due to vast number of parameters and operations
Solution Approach 1:
The patent extracts and removes redundant dimensions from image data through dimensionality reduction techniques, keeping only the most relevant features for classification. This reduces the data volume that needs to be processed by the quantum classifier, thereby decreasing inference time while maintaining classification accuracy.
Solution Approach 2:
The patent transforms image data from classical pixel representations into quantum state representations using quantum feature maps. This parameter transformation enables the system to process image data in quantum space, leveraging quantum parallelism to achieve faster classification without sacrificing accuracy.
2Measurement precision
If full-resolution images are processed, then detailed feature detection is possible, but storage space and processing requirements increase exponentially
Solution Approach 1:
The patent extracts essential visual features from images while discarding redundant information through dimensionality reduction. By identifying and retaining only the most discriminative features needed for classification, the system reduces data volume significantly while preserving feature detection capability.
Solution Approach 2:
The patent transitions image data from classical high-dimensional pixel space to quantum state space, where information is encoded differently using quantum superposition and entanglement. This dimensional transformation allows efficient representation of image features with reduced data requirements.
3Measurement precision
If contrast enhancement is applied to reveal low-contrast features, then feature visibility improves, but processing complexity increases
Solution Approach 1:
The patent applies contrast enhancement and dimensionality reduction as preliminary processing steps before quantum classification. By pre-processing the image data to enhance feature visibility and reduce dimensionality beforehand, the system simplifies the subsequent quantum classification task, reducing overall processing complexity.
Solution Approach 2:
The patent introduces quantum feature maps as an intermediary transformation that maps classical image features into quantum states. This intermediary step enables the quantum classifier to efficiently process enhanced feature information without directly handling the complexity of raw pixel data.
Data Source
AI summary
A computer-implemented method of classifying an image using a quantum trained vision system. The method comprises enhancing contrast of the image and applying dimension reduction to the contrast-enhance image. The enhanced contrast and dimensionally reduced image is passed to a quantum trained vision system and a result is generated from the quantum trained vision system. The result could be a simply yes/no or true/false binary result to see whether the image fell into one of several predefined classes. Alternatively, the result could be an indication of an object in the image.