Quantum Techniques for Image Processing

Ranjan, Arti; Arya, Ashish K. S.; Ravinder, M.

doi:10.1109/icacccn51052.2020.9362910

Cited by 4 publications

(1 citation statement)

References 3 publications

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“…Quantum information processing systems are characterized by features like quantum superposition and quantum entanglement, giving them the potential to enhance image recognition speeds in machine learning. Certain quantum image processing techniques have demonstrated the potential to outpace their classical equivalents dramatically [14][15][16]. Quantum classifiers have been applied to diverse image datasets in the literature [17][18][19][20][21].…”

Section: Dataset and Processing Stepsmentioning

confidence: 99%

Quantum Support Vector Machine for Prostate Cancer Detection: A Performance Analysis

Maouaki,

Said,

Bennai

2024

Preprint

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This study addresses the urgent need for improved prostate cancer detection methods by harnessing the power of advanced technological solutions. We introduce the application of Quantum Support Vector Machine (QSVM) to this critical healthcare challenge, showcasing an enhancement in diagnostic performance over the classical Support Vector Machine (SVM) approach. Our study not only outlines the remarkable improvements in diagnostic performance made by QSVM over the classic SVM technique, but it delves into the advancements brought about by the quantum feature map architecture, which has been carefully identified and evaluated, ensuring it aligns seamlessly with the unique characteristics of our prostate cancer dataset. This architecture succeded in creating a distinct feature space, enabling the detection of complex, non-linear patterns in the data. The findings reveal not only a comparable accuracy with classical SVM (92%) but also a 7.14% increase in sensitivity and a notably high F1-Score (93.33%). This study's important combination of quantum computing in medical diagnostics marks a pivotal step forward in cancer detection, offering promising implications for the future of healthcare technology.

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Section: Dataset and Processing Stepsmentioning

confidence: 99%

Quantum Support Vector Machine for Prostate Cancer Detection: A Performance Analysis

Maouaki,

Said,

Bennai

2024

Preprint

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Applications and Challenges of Quantum Image Processing – A Comprehensive Review

Sungheetha¹

2023

RRRJ

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The tremendous parallel processing capability of quantum computers allow quantum image processing, a multidisciplinary field combining image processing and quantum computing, to expand the potential outcomes for image processing. The problem of quantum computation is to create effective quantum algorithms since quantum computers need extremely effective algorithms than classical algorithm. Additionally, information storage, communication, and computing power are increasing in relevance with the number and importance of processing digital images. Some of these issues might be resolved by encoding the visual data in quantum-mechanical systems as opposed to classical methods and by switching from classical to quantum information processing. Moreover, the quantum image processing is analysed based on the recent research works and is summarized. This study projects the application of quantum image processing and challenges.

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An Analysis of Quantum Computing Spanning IoT and Image Processing

Kamaleswari,

Daniel

2023

Advances in Computer and Electrical Engineering

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The significance of using quantum computing in a variety of applications is illustrated by qubits, coherence time, and gate error rate, respectively, in all of the measurements. In quantum information, the fundamental units are called qubits, which are comparable to bits in classical information. The complicated computation can be simplified by employing qubits, which can be used in a wide variety of contexts. Data can be analyzed without producing any decoherence errors, which is particularly useful for image processing and real-time applications. The concept of “noisy intermediate scale quantum” (NISA) can be addressed and used in real-time data collecting through the Internet of Things as well as in picture processing. The NISA has a primary emphasis on factorization and optimization techniques for the purpose of data analysis. Therefore, this progress will be essential in unlocking the full potential of quantum computing and enabling it to tackle a wider variety of difficult challenges like the Internet of Things (IoT) and Image Processing

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Quantum Techniques for Image Processing

Cited by 4 publications

References 3 publications

Quantum Support Vector Machine for Prostate Cancer Detection: A Performance Analysis

Quantum Support Vector Machine for Prostate Cancer Detection: A Performance Analysis

Applications and Challenges of Quantum Image Processing – A Comprehensive Review

An Analysis of Quantum Computing Spanning IoT and Image Processing

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