Baidu Releases Quantum Machine Learning Toolkit Paddle Quantum On GitHub

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  • It has been built on Baidu’s deep learning platform PaddlePaddle
  • Baidu also announced a set of new tools that will provide a total of 27 enhanced features for the PaddlePaddle framework

Baidu Inc has released a toolkit for quantum machine learning called Paddle Quantum on GitHub. The tool aims to be used by developers to build and train quantum neural network models. It has been built on Baidu’s deep learning platform PaddlePaddle.

Paddle Quantum consists of a set of quantum machine learning toolkits. It includes chemistry library with optimisation tools and three quantum apps — machine learning, chemical simulation, and combinatorial optimisation. Baidu said Paddle Quantum can be used to support quantum circuit models and general quantum computing research.

Duan Runyao, director of Baidu’s Institute for Quantum Computing said that researchers in the quantum field can use the Paddle Quantum to develop quantum artificial intelligence. He added that deep-learning enthusiasts have a shortcut to learning quantum computing.

27 enhanced features for the PaddlePaddle framework

Baidu also announced a set of new tools that will provide a total of 27 enhanced features for the PaddlePaddle framework. These include Paddle.js, which is a deep learning JavaScript library. It enables developers to use AI within a browser or mini-programs for applications like WeChat. It will include another tool called Parakeet which is a text-to-speech toolkit that supports models like ClariNet and WaveFlow. Another tool it will include is Paddle X, which is an integration development tool that helps in faster data processing,

Baidu chief technology officer Wang Haifeng said that it is an unprecedented opportunity for the development of PaddlePaddle due to the rise of industrial intelligence and the acceleration of AI-powered infrastructure. He added that the company will continue to embrace the open-source spirit, drive technological innovation, and partner with developers to advance deep learning and AI technologies and speed up the process of industrial intelligence.

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