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AI-based Solution

Quantum machine learning is an emerging field that takes advantage of quantum computing to improve the performance of traditional artificial intelligence, with applications in computer vision, natural language processing, reinforcement learning, recommender systems, and other directions. The parallel nature of quantum computing can improve the efficiency of neural network training and optimisation, and is also useful for faster and more accurate solving of complex system models. Overall, quantum computing can not only accelerate the computational speed of AI, but also improve the accuracy of its problem processing and decision-making, and promote the innovation and development of AI technology.

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DEEPROUTE.AI

Quantum Computing Enables AI Applications to Embark on A Long Journey

A large number of computations are required during the training process of machine learning models, among which the gradient descent algorithm based on gradient is a widely used method for optimising model parameters. In classical computing, gradient computation needs to repeatedly compute those functions containing parameters, and with the increase of the number of variables in the model, these computations require a large amount of computational resources, which causes a bottleneck of classical arithmetic during the training process of various large models of current classical machine learning. Researchers at SpinQ have proposed a new quantum-based gradient computation method that takes advantage of the high parallelism of quantum computing to obtain the numerical gradient results of multivariate functions with only one computation, providing an efficient solution to the parameter optimisation problem.

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