Determination of quantum entanglement concurrence using multilayer perceptron neural networks.

Siddhartha Bhattacharyya

Joanna Wiśniewska, Marek Sawerwain

C. B. Pronin, Andrey Vladimirovich Ostroukh

This paper highlights a practical research of the possibility of forming quantum circuits for training neural networks. The demonstrated quantum circuits were based on the principles of Grover's Search Algorithm. The perceptron was chosen as the architecture for the example neural network. The multilayer perceptron is a popular neural network architecture due to its scalability and applicability for solving a wide range of problems.

Xiaodie Lin, Zhenyu Chen, Zhaohui Wei

Changpeng Shao

Multilayer perceptron is the most common used class of feed-forward artificial neural network. It contains many applications in diverse fields such as speech recognition, image recognition, and machine translation software. To cater for the fast development of quantum machine learning, in this paper, we propose a new model to study multilayer perceptron in quantum computer. This contains the tasks to prepare the quantum state of the output signal in each layer and to establish the quantum version of learning algorithm about the weights in each layer. We will show that the corresponding quantum versions can achieve at least quadratic speedup or even exponential speedup over the classical algorithms. This provide us an efficient method to study multilayer perceptron and its applications in machine learning in quantum computer. Finally, as an inspiration, an exponential fast learning algorithm (based on Hebb's learning rule) of Hopfield network will be proposed.

Qinxue Meng, Jiarun Zhang, Li Zhao +2 more