Quantum Machine Learning
Optimize legacy machine learning algorithms to greatly improve the computing performance
Industry Background
In recent years, artificial intelligence has been widely used across many areas of social life, with remarkable achievements made in its industrialization. As the data volume rockets, the demand for artificial intelligence computing power is soaring. While traditional CPU chips are incapable of handle the parallel computing of massive heterogeneous data, quantum computing boasts the parallel computing and information processing capabilities that are incomparable for the classical computing technology, and precisely fits in with the development demand of artificial intelligence. In the foreseeable future, despite the fact that quantum computers will not completely take over classical computers, their unique advantages in parallel computing and quantum behavior simulation will play a role in specific scenarios demanding extremely high computing power.
Application Scenarios
Quantum Machine Learning
Optimize legacy machine learning algorithms to greatly improve the computing performance
Pattern Matching
Use the Quantum Fourier Transform to accelerate the classical matching algorithms
Use the quantum exchange test algorithm to realize exponential efficiency improvement
Quantum Neural Networks
Use the quantum neural network for speech recognition, image recognition, image processing and other tasks
Related Products
Image Recognition
Quantum convolutional neural networks are used in image recognition.
Image Restoration
Use the hybrid quantum classical generation network for image restoration
Handwritten Digit Recognition
Use the hybrid quantum classical neural network for the recognition of handwritten numbers
Machine Learning Framework VQNet
A quantum machine learning framework efficiently connecting machine learning and quantum algorithms
Cooperation Cases