Quantum distributed deep learning architectures: Models, discussions, and applications

Kwak, Yunseok; Yun, Won Joon; Kim, Jae Pyoung; Cho, Hyunhee; Park, Jihong; Choi, Minseok; Jung, Soyi; Kim, Joongheon

doi:10.1016/j.icte.2022.08.004

Cited by 20 publications

(8 citation statements)

References 27 publications

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“…The possibilities for quantum-assisted ML frameworks have been expanded by Kwak et al's (2023) investigation of quantum distributed deep learning systems. This study presents potential applications and debates that provide opportunities for further research [29]. Ajagekar and You (2021) demonstrated the applicability of quantum methods to specific domains by proposing a hybrid deep learning solution for fault diagnosis in electrical power systems based on quantum computing [30].…”

Section: Literature Reviewmentioning

confidence: 95%

Utilising Dimensionality Reduction for Improved Data Analysis with Quantum Feature Learning

Sihare

2024

Preprint

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This research explores the potential of quantum computing in data analysis, focusing on the efficient analysis of high-dimensional quantum datasets using dimensionality reduction techniques. The study aims to fill the knowledge gap by developing robust quantum dimensionality reduction techniques that can mitigate noise and errors. The research methodology involved a comprehensive review and analysis of existing quantum dimensionality reduction techniques, such as quantum principal component analysis, quantum linear discriminant analysis and quantum generative models. The study also explored the limitations imposed by NISQ devices and proposed strategies to adapt these techniques to work efficiently within these constraints. The key results demonstrate the potential of quantum dimensionality reduction techniques to effectively reduce the dimensionality of high-dimensional quantum datasets while preserving critical quantum information. The evaluation of quantum principal component analysis, quantum linear discriminant analysis and quantum generative models showed their effectiveness in improving quantum data analysis, particularly in improving simulation speed and predicting properties. Despite the challenges posed by noise and errors, robust quantum dimensionality reduction methods showed promise in mitigating these effects and preserving quantum information. Finally, this research contributes to the advancement of quantum data analysis by presenting a comprehensive analysis of quantum dimensionality reduction techniques and their applications. It highlights the importance of developing robust quantum feature learning methods that can operate efficiently in noisy quantum environments, especially in the NISQ era.

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Section: Literature Reviewmentioning

confidence: 95%

Utilising Dimensionality Reduction for Improved Data Analysis with Quantum Feature Learning

Sihare

2024

Preprint

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“…QML is a training method that is expected to solve some of the limitations of classical ML by replacing neural network calculations with quantum computing [25]. QML applies quantum computing to ML by employing a Parametrized Variational Quantum Circuit (VQC), which mimics classical neural networks with fewer parameters [26]. Also, VQCs are the standard method for creating QNNs on the Noisy Intermediate-Scale Quantum (NISQ) devices that are currently available because they are susceptible to a lot of noise [25], [27].…”

Section: Quantum Machine Learningmentioning

confidence: 99%

HQMC-CPC: A Hybrid Quantum Multiclass Cardiac Pathologies Classification Integrating a Modified Hardware Efficient Ansatz

Shoieb,

Younes,

Youssef

et al. 2024

IEEE Access

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Cardiac pathology classification (CPC) based on the volumetric features of three key heart structures can be extracted from segmented cardiac cine magnetic resonance imaging (CMRI) sequences. Machine learning models have recently become very effective tools for handling these problems. Hybrid quantum methods can be employed to enhance the capacities of classical machine learning models. Here, we propose a hybrid quantum multiclass cardiac pathologies classification (HQMC-CPC) model. In the proposed model, discriminative features are extracted to characterize the shape and function of the heart, combining the clinical features, patient features, and radiomics features. In the proposed quantum circuits, all variational parameters are trainable, and the enhanced variational quantum circuit is employed for efficient neural network learning. Using only thirty feature values as input, we propose a hybrid quantum multiclass cardiac pathologies classification (HQMC-CPC) model based on the proposed modified hardware efficient ansatz (MHEA). The proposed model achieves promising results in training and testing with the Automatic Cardiac Diagnosis Challenge (ACDC 2017) dataset. Experimental results showed that the proposed HQMC-CPC model is able to classify different cardiac pathologies with an average minimum performance gap of 3.19%. The average maximum improvement in terms of accuracy in cardiac pathology classification is 7.77%. Moreover, the proposed HQMC-CPC speeds up the testing process by around 60% and 40% compared to the classical classifiers and well-established HEA respectively.

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“…Prior survey works have covered various QML schemes and their application in various scenarios [39]- [42]. The authors in [40] presented an overview of different quantum learning models, such as quantum neural networks and quantum perceptrons.…”

Section: Existing Surveys On Qml Utilizations In Wireless Systems 1) ...mentioning

confidence: 99%

“…In [41], the authors explored various quantum-based models including quantum Hopfield networks, and focused on optimizing parameters for QML schemes. Besides, the authors in [42] discussed potential applications of QML in different scenarios such as object detection and control. However, the research landscape reveals a noticeable gap in the utilization of QML techniques for optimizing wireless communication systems.…”

Section: Existing Surveys On Qml Utilizations In Wireless Systems 1) ...mentioning

confidence: 99%

“…Due to the aforementioned limitations, it is anticipated that early quantum computing for wireless communications deployments may take the form of cloud computing features to facilitate the functioning of many BSs in certain regions [42]. The advent of quantum fog computing, which can manage several macro and micro BSs, may represent the next major development.…”

Section: Quantum Hardwarementioning

confidence: 99%

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Quantum Machine Learning for Next-G Wireless Communications: Fundamentals and the Path Ahead

Narottama,

Mohamed,

Aïssa

2023

IEEE Open J. Commun. Soc.

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A comprehensive coverage of the state-of-the-art in quantum machine learning (QML) methodologies, with a unique perspective on their applications for wireless communications, is presented. The paper begins by delving into the fundamental principles of quantum computing, and then goes through different operations and techniques that are involved in QML deployments. Subsequently, it provides an in-depth look at various methods peculiar to quantum computing, such as quantum search algorithms, and discusses their potentials towards maximizing the performance of wireless systems. The integration of quantum-based learning models into the existing machine learning methodologies, such as within the frameworks of unsupervised learning and reinforcement learning, are then examined. Taking the viewpoint of wireless communications, diverse studies in the literature that employ QML-based optimization methods are also highlighted. Finally, to ensure the applicability and feasibility of QML for optimizing wireless systems, potential solutions for deployment challenges are addressed.INDEX TERMS Next-generation wireless communications; Quantum machine learning; Optimization of wireless systems.

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Quantum distributed deep learning architectures: Models, discussions, and applications

Cited by 20 publications

References 27 publications

Utilising Dimensionality Reduction for Improved Data Analysis with Quantum Feature Learning

Utilising Dimensionality Reduction for Improved Data Analysis with Quantum Feature Learning

HQMC-CPC: A Hybrid Quantum Multiclass Cardiac Pathologies Classification Integrating a Modified Hardware Efficient Ansatz

Quantum Machine Learning for Next-G Wireless Communications: Fundamentals and the Path Ahead

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