Hybrid Quantum Image Classification With Parallel Circuit Segmentation
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Solution Overview
Problem
Traditional hybrid quantum neural networks for image classification require a large number of qubits and deep quantum gate operations, leading to decoherence effects that reduce efficiency and accuracy.
Innovation Solution
An image classification method using a convolutional neural network module, a quantum layer module, and a classification layer module, where one-dimensional feature data is processed in parallel by multiple quantum circuits, followed by concatenation and loss function calculation to train the model, reducing the number of qubits and depth of quantum circuits.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If hybrid quantum neural networks are used for image classification, then quantum computing capabilities are utilized, but the number of qubits required becomes excessively large
Solution Approach 1:
The patent divides the image classification task into two segments: classical convolutional neural network for feature extraction and quantum neural network for classification. This segmentation allows each component to operate within its optimal resource constraints, with the classical network handling heavy feature extraction and the quantum network performing classification with fewer qubits.
Solution Approach 2:
The patent transforms the high-dimensional image data into a lower-dimensional quantum state space through the classical-quantum interface. The classical CNN extracts essential features and reduces dimensionality before feeding data to the quantum network, effectively changing the dimensional representation to match quantum computational capabilities.
2Power
If deep quantum gate operations are performed, then quantum processing power is increased, but decoherence effects increase and reduce accuracy
Solution Approach 1:
The patent maintains a continuous hybrid architecture where the classical CNN continuously provides preprocessed features to the quantum network, which continuously performs classification. This continuous collaboration allows the system to achieve high processing power through the quantum component without requiring excessively deep quantum circuits, as the classical component continuously supplies refined input.
Solution Approach 2:
The classical convolutional neural network performs preliminary action by extracting and preprocessing features before the quantum network processes the data. This preliminary feature extraction reduces the complexity of input to the quantum network, allowing shallower quantum circuits to achieve the same classification accuracy that would otherwise require deep quantum gate operations.
Data Source
AI summary
The disclosure provides an image classification method, system, electronic device, and storage medium, including: acquiring a sample image and using a convolutional neural network module to convert the sample image into one-dimensional feature data; splitting one-dimensional feature data into multiple data segments, and using multiple quantum circuits in the quantum layer module to process all data segments in parallel; concatenating the output results of all quantum circuits to acquire a concatenated vector, and using the classification layer module to output the category prediction results corresponding to the concatenated vector; calculating the loss function value based on the category prediction results and the category labels of sample images, and train an image classification model; an unknown image corresponding to the image recognition task is determined, and the trained image classification model is used to output the image category of the unknown image.