Advances in Quantum Machine Learning and Deep Learning for Image Classification: A Survey

Kharsa, Ruba; Bouridane, Ahmed; Amira, Abbes

doi:10.1016/j.neucom.2023.126843

Cited by 5 publications

(1 citation statement)

References 49 publications

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“…Quantum machine learning has emerged as a promising tool to enhance classical machine learning techniques [24]. Research indicates that both quantum and quantuminspired computing models have the potential to optimize the training process of conventional models, resulting in improved prediction accuracy for target functions with reduced iteration requirements [25,26].…”

Section: Introductionmentioning

confidence: 99%

Predicting Brain Age and Gender from Brain Volume Data Using Variational Quantum Circuits

Jeon,

Park,

Baek

2024

Brain Sciences

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The morphology of the brain undergoes changes throughout the aging process, and accurately predicting a person’s brain age and gender using brain morphology features can aid in detecting atypical brain patterns. Neuroimaging-based estimation of brain age is commonly used to assess an individual’s brain health relative to a typical aging trajectory, while accurately classifying gender from neuroimaging data offers valuable insights into the inherent neurological differences between males and females. In this study, we aimed to compare the efficacy of classical machine learning models with that of a quantum machine learning method called a variational quantum circuit in estimating brain age and predicting gender based on structural magnetic resonance imaging data. We evaluated six classical machine learning models alongside a quantum machine learning model using both combined and sub-datasets, which included data from both in-house collections and public sources. The total number of participants was 1157, ranging from ages 14 to 89, with a gender distribution of 607 males and 550 females. Performance evaluation was conducted within each dataset using training and testing sets. The variational quantum circuit model generally demonstrated superior performance in estimating brain age and gender classification compared to classical machine learning algorithms when using the combined dataset. Additionally, in benchmark sub-datasets, our approach exhibited better performance compared to previous studies that utilized the same dataset for brain age prediction. Thus, our results suggest that variational quantum algorithms demonstrate comparable effectiveness to classical machine learning algorithms for both brain age and gender prediction, potentially offering reduced error and improved accuracy.

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Section: Introductionmentioning

confidence: 99%

Predicting Brain Age and Gender from Brain Volume Data Using Variational Quantum Circuits

Jeon,

Park,

Baek

2024

Brain Sciences

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Hybrid Neural Networks for Medical Image Classification

Khmelnytskyi,

Stirenko,

Gordienko

2024

Information Systems Engineering and Management

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A Novel Quantum Neural Network Approach to Combating Fake Reviews

Bikku,

Thota,

Shanmugasundaram

2024

Int J Netw Distrib Comput

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As e-commerce has grown gradually online item assessments have emerged as a key source of consumer data. That stated, there are problems with the consistency and fictitiousness of the evaluations because there are numerous fake or fraudulent ones. These misleading assessments are generated during the investigation in an attempt to mislead customers about the nature of a real advantage, compromising their ability to make a predetermined decision and damaging the reputations of businesses. A cutting-edge interrogation department revealed that quantum machine learning (QML) could manage a huge amount of machine-trained data and could convey almost emotional choices in the context of inaccurate checks. It is truly beneficial in obtaining reviews for things that are incorrect. Opinion, generating trends, interpersonal relationships, and assessing fatigue is merely a few of the attributes. Tests conducted utilizing the Amazon fraudulent review. The dataset demonstrates that QML tactics outperform conventional knowledge acquisition procedures in errands, including the place of fraudulent reviews. The delicacy and tolerance of incorrect review distinguishing evidence can be significantly advanced, although QML is still in its early stages of development. Both our proposed model and model pass rigorous conventional machine learning algorithms testing with a remarkable level of accuracy. An article introduces a unique approach to fraudulent review detection and brings in the QNN algorithm as a solution. A deep learning model, such as this one, has an 86% accuracy rate in quantum computer implementation, which is an impressive level of innovation, especially if it comes with successful results. Involvement in these cutting-edge technologies promises significant benefits in battling the problem of false identities on the Web. In our case, the experimental results demonstrate that our QNN algorithm, which can accurately identify fake reviews, will become a key weapon for suppressing various forms of fraudulence on emerging digital technology platforms.

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Advances in Quantum Machine Learning and Deep Learning for Image Classification: A Survey

Cited by 5 publications

References 49 publications

Predicting Brain Age and Gender from Brain Volume Data Using Variational Quantum Circuits

Predicting Brain Age and Gender from Brain Volume Data Using Variational Quantum Circuits

Hybrid Neural Networks for Medical Image Classification

A Novel Quantum Neural Network Approach to Combating Fake Reviews

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