Quantum computation via neural networks applied to image processing and pattern recognition

Alexandr Alexandrovich Ezhov

Samir Khalid Akhmed, S. V. Skorodumov, S.K. Akhmed

Three computational models are considered: classical, hybrid (NISQ) and quantum computational models, their pros, cons, possibilities of implementation in modern realities and the problem of image classification and its solution using neural networks in these computational models. Three computational experiments were carried out using the described image recognition approaches with visualization of the learning process and a comparison of the final metrics was carried out aimed at clarifying the prospects of the applied approach based on quantum computing.

Ding Qiu-lin

The storage capacity of the conventional Hopfield network is the 0.14 times of the number of neurons(P=0.14N). Because of the huge difficulty in recognizing a large number of images or patterns, researchers are looking for new methods at all times. Quantum neural network (QNN) is a young and outlying science built upon the combination of classical neural network and quantum computing. A Quantum Hopfield neural network (QHNN) whose elements of the storage matrix are distributed in a probability way on the base of quantum linear superposition is presented for speeding up the images recognition and increasing the number of the images recognition. Contrasting to the conventional Hopfield neural network, the storage capacity of the QHNN is increased by a factor of 2N, where N is the number of neurons. Besides, the case analysis and simulation tests have been carried out for the recognition of images in this paper. The result indicates that QHNN can recognize the images or patterns effectively and its working process accords with quantum evolvement process.

Vladimir G. Ivancevic, Tijana T. Ivancevic

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

Jing Wang, Meng Zhang, Junsen Lai +1 more

Quantum Convolutional Neural Network (QCNN) is an integration of classical convolutional neural networks and quantum computing, designed to leverage the unique advantages of quantum computing to enhance machine learning models. It holds promise for providing novel perspectives in solving complex pattern recognition tasks. In the process of image recognition, factors such as the type of convolutional layers, pooling layers, and sample sets significantly influence the recognition results. This study conducts experimental validation on QCNN-based image recognition, investigating the impact of different convolutional layer types, pooling layer types, and image sample datasets on the accuracy of QCNN recognition. Additionally, future development trends of QCNN are discussed.