Quantum Neural Networks: Concepts, Applications, and Challenges

Kwak, Yunseok; Yun, Won Joon; Jung, Soyi; Kim, Joongheon

doi:10.1109/icufn49451.2021.9528698

Cited by 49 publications

(17 citation statements)

References 30 publications

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“…Afterward, the expected value of the Hamiltonian is used to obtain the transformed qubit state vector using Pauli gates. The outputs serve as input to the next layer of perceptrons after decoding the data [11].…”

Section: B Quantum-assisted Neural Networkmentioning

confidence: 99%

Quantum-Assisted Activation for Supervised Learning in Healthcare-Based Intrusion Detection Systems

Laxminarayana

Mishra

Tiwari

et al. 2024

IEEE Trans. Artif. Intell.

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Section: B Quantum-assisted Neural Networkmentioning

confidence: 99%

Quantum-Assisted Activation for Supervised Learning in Healthcare-Based Intrusion Detection Systems

Laxminarayana

Mishra

Tiwari

et al. 2024

IEEE Trans. Artif. Intell.

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“…For the normalization of the features, two main approaches are commonly used: Z-score [ 29 ] and Unity Standard Deviation (SD) [ 30 ]. Finally, in the classification stage, different models are utilized such as: Multilayer Perceptron Neural Network (MLPNN) [ 31 ], Quantum Neural Network (QNN) [ 32 ], Radial Basis Function Neural Network (RBFNN) [ 33 ], Fuzzy C-Means Clustering (FCM) [ 34 ], ID3 Decision Tree [ 35 ], Support Vector Machine (SVM) [ 36 ], Type2 Fuzzy Clustering Neural Network (T2FCNN) [ 37 ], and Probabilistic Neural Network (PNN) [ 38 ].…”

Section: Background and Related Workmentioning

confidence: 99%

ECG Classification Using an Optimal Temporal Convolutional Network for Remote Health Monitoring

Ismail

Jovanovic

Ramzan

et al. 2023

Sensors

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Increased life expectancy in most countries is a result of continuous improvements at all levels, starting from medicine and public health services, environmental and personal hygiene to the use of the most advanced technologies by healthcare providers. Despite these significant improvements, especially at the technological level in the last few decades, the overall access to healthcare services and medical facilities worldwide is not equally distributed. Indeed, the end beneficiary of these most advanced healthcare services and technologies on a daily basis are mostly residents of big cities, whereas the residents of rural areas, even in developed countries, have major difficulties accessing even basic medical services. This may lead to huge deficiencies in timely medical advice and assistance and may even cause death in some cases. Remote healthcare is considered a serious candidate for facilitating access to health services for all; thus, by using the most advanced technologies, providing at the same time high quality diagnosis and ease of implementation and use. ECG analysis and related cardiac diagnosis techniques are the basic healthcare methods providing rapid insights in potential health issues through simple visualization and interpretation by clinicians or by automatic detection of potential cardiac anomalies. In this paper, we propose a novel machine learning (ML) architecture for the ECG classification regarding five heart diseases based on temporal convolution networks (TCN). The proposed design, which implements a dilated causal one-dimensional convolution on the input heartbeat signals, seems to be outperforming all existing ML methods with an accuracy of 96.12% and an F1 score of 84.13%, using a reduced number of parameters (10.2 K). Such results make the proposed TCN architecture a good candidate for low power consumption hardware platforms, and thus its potential use in low cost embedded devices for remote health monitoring.

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“…Quantum neural networks [16][17][18][19] are generally based on variational quantum circuits [20] and can be described by…”

Section: Explainability Of Quantum Neural Networkmentioning

confidence: 99%

On the explainability of quantum neural networks based on variational quantum circuits

Daskin¹

2023

Preprint

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Ridge functions are used to describe and study the lower bound of the approximation done by the neural networks which can be written as a linear combination of activation functions. If the activation functions are also ridge functions, these networks are called explainable neural networks.In this paper, we first show that quantum neural networks which are based on variational quantum circuits can be written as a linear combination of ridge functions. Consequently, we show that the interpretability and explainability of such quantum neural networks can be directly considered and studied as an approximation with the linear combination of ridge functions.

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Quantum Neural Networks: Concepts, Applications, and Challenges

Cited by 49 publications

References 30 publications

Quantum-Assisted Activation for Supervised Learning in Healthcare-Based Intrusion Detection Systems

Quantum-Assisted Activation for Supervised Learning in Healthcare-Based Intrusion Detection Systems

ECG Classification Using an Optimal Temporal Convolutional Network for Remote Health Monitoring

On the explainability of quantum neural networks based on variational quantum circuits

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