MicroAlgo Inc. Develops Quantum Convolutional Neural Network Architecture to Enhance the Performance of Traditional Computer Vision Tasks Using Quantum Mechanics Principles

Published: 2025-05-12 15:00:00 ET MLGO

Published on 05/12/2025 at 15:00, updated on 05/12/2025 at 16:52

MicroAlgo Inc. announced their research on quantum visual computing, exploring the integration of quantum computing with classical convolutional neural networks. They are developing a Quantum Convolutional Neural Network (QCNN) architecture to enhance the performance of traditional computer vision tasks using quantum mechanics principles. The Quantum Convolutional Neural Network ("QCNN") architecture is an innovative computational model that cleverly combines the parallelism of quantum computing with the feature extraction capabilities of classical convolutional neural networks".

In QCNN, quantum bits (qubits) serve as the basic carrier of information, utilizing the properties of quantum superposition and entanglement to achieve parallel processing of multiple computational tasks. At the same time, drawing inspiration from the structure of classical convolutional neural network--such as convolution layers, pooling layers, and fully connected layers--QCNN extracts features, reduces dimensions, and classifies image data, thereby enhancing both computational speed and image recognition accuracy. Computer vision aims to enable computers to understand and analyze visual data, such as images or videos, much like the human visual system, involving tasks such as image recognition, object detection, and image segmentation.

Quantum computing, with its unique quantum properties like superposition and entanglement, eliminates powerful parallel computing capabilities and specialized methods of information processing. Data Preparation: Image or video data is collected from multiple channels, then screened and organized to remove low-quality or non-compliant data. The remaining data is preprocessed, including normalizing pixel values, resizing images, and correcting and enhancing colors to meet the specifications for subsequent processing.

Quantum State Encoding: Following specific rules, the preprocessed image features are mapped onto quantum bits and converted into quantum states. Outputs such as target categories, locations, and other relevant information are provided, while the entire process is optimized based on application feedback. MicroAlgo's QCNN architecture has broad application prospects in the field of computer vision.

In autonomous driving, QCNN can enable fast and accurate recognition of key elements such as road signs, vehicles, and pedestrians, enhancing the safety and reliability of autonomous driving systems. In medical imaging analysis, QCNN can achieve rapid and accurate diagnosis of medical images, assisting doctors in disease diagnosis and treatment planning. In security surveillance, QCNN can enable real-time detection and early warning of abnormal behavior in surveillance videos, improving the efficiency and accuracy of security measures. Additionally, QCNN can be widely applied in various fields such as smart manufacturing, aerospace, and smart cities, driving technological upgrades and intelligent transformations in related industries.