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“本源悟空”科研团队加速拓展量子人工智能场景应用
Zhong Guo Xin Wen Wang· 2025-07-06 14:20
Core Insights - The integration of quantum computing and artificial intelligence (AI) is seen as a crucial direction for the next computing revolution, addressing the exponential growth in computational complexity faced by AI in high-dimensional data processing and complex optimization problems [1][2] - Benyuan Quantum is leveraging its third-generation superconducting quantum computer "Benyuan Wukong" to collaborate with relevant units to expand quantum AI applications, transitioning technology from laboratory validation to industrial implementation [1] Group 1: Quantum Computing and AI Integration - Quantum computing offers inherent advantages in parallel computing and global optimization tasks, providing a new pathway to overcome traditional computational limitations [1] - The research team at Benyuan Quantum has developed a quantum neural network image recognition algorithm that significantly outperforms traditional models in complex image recognition tasks [1] Group 2: Applications in Healthcare - The focus is on applying quantum AI in the medical field, particularly in MRI image reconstruction, addressing clinical challenges such as slow imaging speed and balancing image quality with acceleration efficiency [1] - The integration of quantum AI can enhance the accuracy of MRI image reconstruction through a quantum-classical hybrid neural network architecture, improving image reconstruction precision [1] - The deep integration of quantum computing and AI can significantly improve the accuracy of breast cancer screening using mammography, aiding doctors in reducing misdiagnosis and missed diagnosis rates [2]
玻色子采样用于量子AI图像识别 为现实应用打开新窗口
Ke Ji Ri Bao· 2025-06-29 23:22
Group 1 - The core viewpoint of the articles highlights the significant advancement in quantum artificial intelligence (AI) through the application of boson sampling for image recognition, marking a crucial step towards practical quantum AI applications in real-world scenarios [1][2]. - The research team from Okinawa Institute of Science and Technology successfully utilized a quantum AI system for image classification using only three photons and a linear optical network, demonstrating the potential for low-energy, hybrid quantum methods [1][2]. - The developed quantum AI architecture compresses grayscale image data into single-photon quantum states, which are then processed through a complex optical network to create high-dimensional patterns, achieving superior accuracy compared to traditional machine learning methods [2]. Group 2 - The principle of boson sampling is explained through an analogy of a "marble board" game, illustrating how photons, unlike marbles, exhibit wave-like behavior that leads to complex interference patterns that are difficult to predict even for supercomputers [2]. - The experimental results indicate that the quantum AI system outperformed traditional machine learning approaches across all tested image datasets, showcasing its effectiveness in image recognition tasks [2].