A shallow hybrid classical–quantum spiking feedforward neural network for noise-robust image classification

Konar, Debanjan; Sarma, Aditya Das; Bhandary, Soham; Bhattacharyya, Siddhartha; Cangi, Attila; Aggarwal, Vaneet

doi:10.1016/j.asoc.2023.110099

Cited by 20 publications

(8 citation statements)

References 38 publications

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“…In this approach, classical machine learning techniques are integrated with quantum algorithms to leverage the computational advantages offered by quantum computing while mitigating the limitations of current quantum hardware. [31]. However, it's noteworthy that the majority of QCNN structures are inherently serial, a characteristic that signi cantly ampli es trainability and expressivity [32], albeit at the cost of heightened susceptibility to over tting and increased computational complexity.…”

Section: Related Workmentioning

confidence: 99%

Parallel Structure of Hybrid Quantum-Classical Neural Networks for Image Classification

Xu,

Hu,

Yang

et al. 2024

Preprint

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Hybrid quantum-classical neural networks (QCNNs) integrate principles from quantum computing principle and classical neural networks, offering a novel computational approach for image classification tasks. However, current QCNNs with sequential structures encounter limitations in accuracy and robustness, especially when dealing with tasks involving numerous classes. In this study, we propose a novel solution - the hybrid Parallel Quantum Classical Neural Network (PQCNN) - for image classification tasks. This architecture seamlessly integrates the parallel processing capabilities of quantum computing with the hierarchical feature extraction abilities of classical neural networks, aiming to overcome the constraints of conventional sequential structures in multi-class classification tasks. Extensive experimentation demonstrates the superiority of PQCNN over traditional concatenative structures in binary classification datasets, displaying heightened accuracy and robustness against noise. Particularly noteworthy is PQCNN's significantly improved accuracy on datasets with 5 and 10 classes. These findings underscore the transformative potential of the PQCNN architecture as an advanced solution for enhancing the performance of quantum-classical-based classifiers, particularly in the domain of image classification.

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Section: Related Workmentioning

confidence: 99%

Parallel Structure of Hybrid Quantum-Classical Neural Networks for Image Classification

Xu,

Hu,

Yang

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

“…Building on these findings, Konar et al (2023) present a groundbreaking approach to noise-robust image classification with their shallow hybrid classical-quantum spiking feedforward neural network (SQNN). This novel SQNN combines the robustness of classical spiking neural networks (SFNN) with the computational efficiency of quantum circuits, in particular Variational Quantum Circuits (VQC), to significantly improve the performance in processing noisy data.…”

Section: Comparative Analyzesmentioning

confidence: 99%

Machine Learning Applications of Quantum Computing: A Review

Nguyen,

Sipola,

Hautamäki

2024

eccws

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At the intersection of quantum computing and machine learning, this review paper explores the transformative impact these technologies are having on the capabilities of data processing and analysis, far surpassing the bounds of traditional computational methods. Drawing upon an in-depth analysis of 32 seminal papers, this review delves into the interplay between quantum computing and machine learning, focusing on transcending the limitations of classical computing in advanced data processing and applications. This review emphasizes the potential of quantum-enhanced methods in enhancing cybersecurity, a critical sector that stands to benefit significantly from these advancements. The literature review, primarily leveraging Science Direct as an academic database, delves into the transformative effects of quantum technologies on machine learning, drawing insights from a diverse collection of studies and scholarly articles. While the focus is primarily on the growing significance of quantum computing in cybersecurity, the review also acknowledges the promising implications for other sectors as the field matures. Our systematic approach categorizes sources based on quantum machine learning algorithms, applications, challenges, and potential future developments, uncovering that quantum computing is increasingly being implemented in practical machine learning scenarios. The review highlights advancements in quantum-enhanced machine learning algorithms and their potential applications in sectors such as cybersecurity, emphasizing the need for industry-specific solutions while considering ethical and security concerns. By presenting an overview of the current state and projecting future directions, the paper sets a foundation for ongoing research and strategic advancement in quantum machine learning.

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“…Due to insufficient training data from previous experiments and severe iterations that were necessary to ensure accurate outcome prediction [21]. In the same year, Konar et al presented a "classical and quantum spiked hybrid shallow neural network for noise-robust image classification" in which feed-forward neural networks were used [22].…”

Section: Introductionmentioning

confidence: 99%

Seasonal Decomposition and Trend using Hybrid STL- FNN with Application

2023

Advances in the Theory of Nonlinear Analysis and Its Application

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Most researchers in the field of time series follow one approach, which is to rely on time series models in general. Several models have emerged in engineering, economics, and health applications. These models are known as neural networks, which imitate human nerve cells. This study suggests a hybrid algorithm called STL-FNN. It combines two methods -seasonal trend decomposition STL and feed-forward neural network (FNN). The STL method analyzes the original data into three subseries: seasonality, trend, and residual. To predict each of the three sub-series, the FNN neural network uses the seasonal component and separates them. Finally, all predicted outputs are combined as a total time series product. The results indicate that the STL-FNN can check the performance of the hybrid model. It uses the relative absolute error (MAE) criterion. We analyzed actual data to test the hybrid model's ability to predict. The model used the average monthly spending of foreign visitors in the United Kingdom from January 1986 to February 2020.

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A shallow hybrid classical–quantum spiking feedforward neural network for noise-robust image classification

Cited by 20 publications

References 38 publications

Parallel Structure of Hybrid Quantum-Classical Neural Networks for Image Classification

Parallel Structure of Hybrid Quantum-Classical Neural Networks for Image Classification

Machine Learning Applications of Quantum Computing: A Review

Seasonal Decomposition and Trend using Hybrid STL- FNN with Application

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