Quantum M-P Neural Network

G. Bonnell, G. Papini

Xuan Hou

This paper has been studied in Quantum Learning Vector Quantization Neural Network. The concept of competition and model of competitive network has been studied. It analyses model of Quantum Learning Vector Quantization Neural Network and LVQ algorithm. Quantum Learning Vector Quantization Neural Network is a hybrid neural network and quantum computing theory is used in the Quantum Learning Vector Quantization Neural Network directly to build Quantum Learning Vector Quantization Neural Network. Model of Quantum Learning Vector Quantization Neural Network and Quantum-LVQ algorithms are proposed. In the study of clustering problem, experiments show that Quantum Learning Vector Quantization Neural Network have an advantage over than Learning Vector Quantization Neural Network.

Andrew Haverly, Shahram Rahimi, M. A. Novotny

This paper introduces an application of Grover’s algorithm to optimize neural network training by eliminating the computationally demanding backward propagation. It clarifies previous assertions regarding training neural networks with Grover’s algorithm. The paper introduces parallel data training and training on exponentially seeded datasets. A quantum binary neural network is created to more efficiently implement neural networks on quantum computers. This trained discriminatory quantum binary neural network is shown to behave as a generative quantum binary neural network. We present methods that both simplify and enhance the process of neural network development and training through Grover’s algorithm. The quantum binary neural network has the potential to far outpace classical neural network training in terms of training time and energy.

IT Informatics

Sanjay Gupta, Royce K. P. Zia

Yu-Chao Dong, Xikun Li, Ming Yang +4 more

Abstract To efficiently complete quantum information processing tasks, quantum neural networks (QNNs) should be introduced rather than the common classical neural networks, but the QNNs in the current noisy intermediate-scale quantum era cannot perform better than classical neural networks because of scale and the efficiency limits. So if the quantum properties can be introduced into classical neural networks, more efficient classical neural networks may be constructed for tasks in the field of quantum information. Complex numbers play an indispensable role in the standard quantum theory, and constitute an important feature in quantum theory. So if complex numbers are introduced in classical neural networks, they may outperform the common classical neural networks in dealing with the tasks in the quantum information field. In this paper, we verify this conjecture by studying quantum state classification via complex-valued neural networks (CVNNs). The numerical results show that the performance of CVNNs is much better than the real-valued neural network in classifying the entangled states. Our results not only provide a new way to improve the performance of artificial neural networks in quantum state classifiers, but also might shed light on the study of CVNNs in the field of other quantum information processing tasks before the appearance of the universal quantum computer.