A Novel Neural Network Based on Quantum Computing

Aswathy Ajayan, Alex Pappachen James

Abstract The extreme parallelism property warrant convergence of neural networks with that of quantum computing. As the size of the network grows, the classical implementation of neural networks becomes computationally expensive and not feasible. In this paper, we propose a hybrid image classifier model using spiking neural networks (SNN) and quantum circuits that combines dynamic behaviour of SNN with the extreme parallelism offered by quantum computing. The proposed model outperforms models in comparison with spiking neural network in classical computing, and hybrid convolution neural network-quantum circuit models in terms of various performance parameters. The proposed hybrid SNN-QC model achieves an accuracy of 99.9% in comparison with CNN-QC model accuracy of 96.3%, and SNN model of accuracy 91.2% in MNIST classification task. The tests on KMNIST and CIFAR-1O also showed improvements.

Sonia Maria D’Souza, R. Ramesh Prasad, Chethan S. Reddy +3 more

Moolchand Sharma, Nebojša Bačanin, Tarik Ahmed Rashid

Hugo de GARIS, Zijun Zhang

This dissertation weds three specialties---Evolutionary Computation (EC), Neural Networks (NN), and Computing (QC)---to form a new triple Quantum Evolution of Neural Networks (QENN). After several review chapters on the principles of evolutionary algorithms, neural networks, quantum computing, etc., quantum genetic operators are defined, and several new pure and hybrid quantum/classical evolutionary algorithms are introduced that are considerably faster than their classical equivalents. Several concrete examples of the quantum evolution of very simple neural networks are also provided. Another approach to quantum neural network evolution, called Neuron Evolution (QNE), is introduced.

Subhash C. Kak

Avinash Chalumuri, Raghavendra Kune, B. S. B. S. Manoj

Artificial neural networks (ANN) proved to be efficient in solving many problems for big data analytics using machine learning. The complex and non-linear features of the input data can be learned and generalized by ANN. In the big data era, enormous amounts of data arrive from multiple sources. A stage is expected to be reached where even supercomputers are likely inundated with the big data. Training an ANN in such a situation is a challenging task due to the size and dimension of the big data. Also, a large number of parameters are to be used and optimized in the network to learn the patterns and analyze such data. Quantum computing is emerging as a field that provides a solution to this problem as a quantum computer can represent data differently using qubits. Qubits on quantum computers can be used to detect the hidden patterns in data that are difficult for a classical computer to find. Hence, there exists a huge scope for application in the area of artificial neural networks. In this work, we primarily focused on training an artificial neural network using qubits as artificial neurons. The simulation results show that our quantum computing approach for ANN (QC ANN) is efficient when compared to classical ANN. The model with qubits as artificial neurons can learn the features of data using fewer parameters for a binary classification task. We demonstrate our experiment using a quantum simulator and optimization of the quantum parameters used in QC ANN is carried out on a classical computer.