InfoQ Homepage News Paddle Quantum: Bringing Baidu Deep Learning Perform to Quantum Computing

Paddle Quantum: Bringing Baidu Deep Learning Perform to Quantum Computing


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 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.

With Paddle Quantum, we can translate this problem into a quantum neural network to train an optimal model. Then we can either find the solution by a classical simulation of the model, or run the model directly on a quantum computer. We are reducing the number of layers in a quantum computing network by 50%, making our approach more flexible in deployment compared to others in the industry.

Paddle Quantum GitHub repo includes several tutorials using Jupiter notebooks and a number of examples, including QAQA.

Paddle Quantum is based on a new version of PaddlePaddle, which includes 39 new algorithms and brings the total number of available algorithms to 146. The latest PaddlePaddle also includes a Paddle.js library that enables the use of AI in the browser; Parakeet, a text-to-speech toolkit; PaddleClass, a library for image classification supporting many classification networks; and PaddleCloud, which is aimed to make it simpler to run deep learning tasks and take advantage of cloud scalability.

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