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Journal of Intelligent & Fuzzy Systems

A quantum classification algorithm for classification incomplete patterns based on entanglement measure

Abdel‐Haleem Abdel‐Aty, Heba Kadry, Mohammed Zidan, Yazeed A. Al-Sbou, Elnomery Allam Zanaty, Mahmoud Abdel‐Aty

Published Dec 31, 2019• 51 citations
Publisher

Abstract

In this paper, a novel quantum classification algorithm that is based on competitive learning is presented to classify an input pattern that results from the failures of some sensors. As long as an incomplete pattern is presented to our model, the proposed algorithm performs the competitions between the neurons by applying some unitary transformations then measures the degree of entanglement using concurrence measure to find the winner class based on the winner-take-all technique. The proposed algorithm finds the most likely winning class label in between two binary competitive classes for an incomplete pattern presented to the proposed model. Because larger scale quantum computers are still in the lab, we studied the proposed algorithm on a case study.

DOI
10.3233/jifs-179566

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In this paper, a novel quantum classification algorithm that is based on competitive learning is presented to classify an input pattern that results from the failures of some sensors. As long as an incomplete pattern is presented to our model, the proposed algorithm performs the competitions between the neurons by applying some unitary transformations then measures the degree of entanglement using concurrence measure to find the winner class based on the winner-take-all technique. The proposed algorithm finds the most likely winning class label in between two binary competitive classes for an incomplete pattern presented to the proposed model. Because larger scale quantum computers are still in the lab, we studied the proposed algorithm on a case study.
In this paper, we develop a novel classification algorithm that is based on the integration between competitive learning and the computational power of quantum computing. The proposed algorithm classifies an input into one of two binary classes even if the input pattern is incomplete. We use the entanglement measure after applying unitary operators to conduct the competition between neurons in order to find the winning class based on wining-take-all. The novelty of the proposed algorithm is shown in its application to the quantum computer. Our idea is validated via classifying the state of Reactor Coolant Pump of a Risky Nuclear Power Plant and compared with other quantum-based competitive neural networks model.
Quantum neural network is a fledging research domain based on the merge of classical neural network and quantum computing. In this paper, we add the quantum effect to the classical hamming neural network algorithm in order to employ the advantages of quantum information to yield, finally, a novel quantum competitive learning algorithm. The proposed algorithm, called quantum hamming neural network (QHNN), is capable to recognize incomplete patterns, as well as, increase the probability of recognizing patterns on the account of undesired patterns. Moreover, these undesired patterns could be used as new patterns for training the algorithm in subsequent steps. The proposed algorithm is testified via a case study and a classification experiment, where promising results, reaches to 100%, are given and compared favorably with other reported quantum competitive algorithms.
Quantum Computing and especially Quantum Machine Learning, in a short period of time, has gained a lot of interest through research groups around the world. This can be seen in the increasing number of proposed models for pattern classification applying quantum principles to a certain degree. Despise the increasing volume of models, there is a void in testing these models on real datasets and not only on synthetic ones. The objective of this work is to classify patterns with binary attributes using a quantum classifier. Specially, we show results of a complete quantum classifier applied to image datasets. The experiments show favorable output while dealing with balanced classification problems as well as with imbalanced classes where the minority class is the most relevant. This is promising in medical areas, where usually the important class is also the minority class.
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This paper combines quantum computation with conventional artificial neural network theory to present a novel network - self-organizing quantum neural network (SOQNN) that can perform pattern classification self-organizationally and automatically through quantum competitive process. It does not need to prestore some given patterns but can classify input patterns with higher classification rapidity than that of classical neural networks(CNN).Further, SOQNN make the analogical pattern win easily in the next quantum competitive process by modifying the value of the quantum register(QR) corresponding to the pattern classified.