Student Publications [Scholarly]

Network of Quantum Neural Networks

Document Type

Conference Proceeding

Abstract

The foundational work on theory and applications of artificial intelligence using quantum computations started to expand with new foundational work introduced recently. The introduction of quantum neural network (QNN) is recent, and its applications are shown to provide strong results in real-life cases. In this work, we introduce a new theoretical framework; Network of quantum neural network (N-QNN) as a new quantum-based deep learning technique that takes advantage of quantum and traditional methods that run interconnected subnetworks that are formed by QNN, other quantum techniques as well as traditional AI methods. This new technique’s subnetwork builds on mathematical foundations of quantum neural network that focuses on minimizing the difference between the calculated and known values of the network output by utilizing a cost function; Hence, this new design utilizes QNNs as a part of subnetwork design instead of using only qubits alongside of other methods such as Artificial Neural Networks (ANN), Support Vector Machines (SVM), and Convolutional Neural Network (CNN) as well as other quantum methods such as quantum convolutional neural network (QCNN) and Quantum Support Vector Machine (QSVM); Hence, we introduce different subnetwork designs of N-QNN that utilizes quantum and traditional techniques. N-QNN’s subnetwork design is tested on the well-known Iris data set by using 3-class classification. N-QNN’s result comparison with traditional ANN indicated a better accuracy attainment 97% along with faster result achievement (less than two minutes) by using a quantum simulator. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2027.

Publication Title

Communications in Computer and Information Science

Publication Date

2027

Volume

2967 CCIS

First Page

436

Last Page

448

ISSN

1865-0929

ISBN

9783032284532

DOI

10.1007/978-3-032-28454-9_30

Keywords

Artificial Neural Networks, efficiency, Network of Artificial Neural Networks, quantum computing, quantum simulator

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