Baidu announced Paddle Quantum, an open source machine learning toolkit designed to help data scientists train and develop AI within quantum computing applications. It’s built atop Baidu’s PaddlePaddle deep learning platform. Baidu claims it’s “more flexible” compared with other quantum computing suites, reducing the complexity of one popular algorithm — quantum approximate optimization algorithm (QAOA) — by a claimed 50%.
Over 1.9 million developers use PaddlePaddle, according to Baidu, and 84,000 enterprises have created more than 230,000 models with the framework since its debut. The framework was open-sourced in 2016.
Paddle Quantum supports three types of quantum applications: quantum machine learning, quantum chemical simulation, and quantum combinatorial optimization.
It includes resources addressing challenges like combinatorial optimization problems and quantum chemistry simulations, as well as complex variable definitions and matrix multiplications enabling quantum circuit models and general quantum computing. It also features an implementation of QAOA that translates into a quantum neural network by identifying a model through classical simulation or running directly on a quantum computer.
The latest version of its PaddlePaddle machine learning framework, which over the past few months has gained 39 new algorithms for a total of 146 and more than 200 pretrained models.
Other AI quantum framework are Google’s TensorFlow Quantum, Facebook’s PyTorch in Pennylane an Microsoft libraries for quantum ML. (Venturebeat)