Baidu Bringing Deep Learning ‘Paddle Quantum’ to Quantum Computing

(Infoq.com) Baidu has announced quantum machine learning toolkit Paddle Quantum, which makes it possible to build and train quantum neural network models. Paddle Quantum aims to support advanced quantum computing applications as well as to allow developers new to quantum machine learning to create their models step-by-step.
From now on, researchers in the quantum field can use the Paddle Quantum to develop quantum artificial intelligence, and our deep learning enthusiasts have a shortcut to learning quantum computing.
Based on Baidu deep learning platform PaddlePaddle, Paddle Quantum targets at the moment three major applications, quantum machine learning, quantum chemical simulation, and quantum combinatorial optimization. To this aim, it includes several different tools, including a quantum chemistry library, combinatorial optimization tools, and others.
Along with Paddle Quantum, Baidu disclosed a novel implementation of the Quantum Approximate Optimization Algorithm (QAOA), which was proposed in 2014 to solve NP-hard Maximum cut problem. The Max-cut problem consists in finding a subset S of a graph’s vertices such that the number of edges between S and the complementary subset is as large as possible. This problem has applications in theoretical physics, VLSI circuit design, and other fields.