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Advances in computer and electrical engineering book series

Examining Quantum Algorithms for Quantum Image Processing

Hai-Sheng Li

Published Aug 26, 2020• 19 citations
Publisher

Abstract

"This book provides a comprehensive introduction to quantum image algorithms to establish frameworks of quantum image processing" - Provided by publisher
DOI
10.4018/978-1-7998-3799-2

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This book covers quantum mechanics and its applications across multiple fields, starting with parameter estimation and probability density functions, and examines quantum systems, statistical image processing, and mathematical frameworks. The book connects theoretical physics with applications in robotics, neural networks, and computer vision. Quantum mechanics with stochastic calculus and filtering theory Radon transform applications in image reconstruction Quantum field theories and chiral anomalies Algebraic geometry and group theory applications Supersymmetry, cohomology, and quantum effective action Suitable for graduate students and researchers in physics, mathematics, and computer science, this book provides theoretical foundations and practical applications for understanding quantum systems and computational implementations. This title has been co-published with Manakin Press. Taylor & Francis does not sell or distribute the print edition in India, Pakistan, Nepal, Bhutan, Sri Lanka and Bangladesh.

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Introduction and Background

Phillip Kaye, Raymond Laflamme, Michele Mosca

A computer is a physical device that helps us process information by executing algorithms. An algorithm is a well-defined procedure, with finite description, for realizing an information-processing task. An information-processing task can always be translated into a physical task. When designing complex algorithms and protocols for various information-processing tasks, it is very helpful, perhaps essential, to work with some idealized computing model. However, when studying the true limitations of a computing device, especially for some practical reason, it is important not to forget the relationship between computing and physics. Real computing devices are embodied in a larger and often richer physical reality than is represented by the idealized computing model. Quantum information processing is the result of using the physical reality that quantum theory tells us about for the purposes of performing tasks that were previously thought impossible or infeasible. Devices that perform quantum information processing are known as quantum computers. In this book we examine how quantum computers can be used to solve certain problems more efficiently than can be done with classical computers, and also how this can be done reliably even when there is a possibility for errors to occur. In this first chapter we present some fundamental notions of computation theory and quantum physics that will form the basis for much of what follows. After this brief introduction, we will review the necessary tools from linear algebra in Chapter 2, and detail the framework of quantum mechanics, as relevant to our model of quantum computation, in Chapter 3. In the remainder of the book we examine quantum teleportation, quantum algorithms and quantum error correction in detail. We are often interested in the amount of resources used by a computer to solve a problem, and we refer to this as the complexity of the computation. An important resource for a computer is time. Another resource is space, which refers to the amount of memory used by the computer in performing the computation. We measure the amount of a resource used in a computation for solving a given problem as a function of the length of the input of an instance of that problem.

Oxford University Press eBooks•Nov 16, 2006•0 citations

Exploring the research scope in Satellite Imagery Using Quantum Image processing - A comprehensive Review

Lakshmi Devi Pujari, Sridhar C. Naga Venkata, Saahit C. Naga Venkata

Quantum computing (QC) promises to revolutionize data-intensive fields due to its potential for exponential speedups and novel computational paradigms. Satellite image processing (also called Earth Observation, EO) is one such domain, where large volumes of high-dimensional data (e.g., multispectral or hyperspectral images) challenge classical methods in terms of computation, memory, and latency. In this review, we survey the state-of-the-art at the intersection of quantum computing and satellite image processing. We first outline quantum image representations, key quantum algorithms proposed for image processing (e.g., edge detection, segmentation, classification), and quantum machine learning (QML) models. We then examine recent studies that target Earth observation data, including resource estimation and hybrid quantum-classical architectures. Next, we delineate the major challenges (hardware, encoding, noise, scalability) and propose potential solutions, focusing on suitable quantum algorithms and hybrid strategies. Finally, we discuss future directions and research opportunities.

•Jan 1, 2025•0 citations

Review of Quantum Image Processing

Zhaobin Wang, Minzhe Xu, Yaonan Zhang

Archives of Computational Methods in Engineering•May 14, 2021•121 citations
This book covers quantum mechanics and its applications across multiple fields, starting with parameter estimation and probability density functions, and examines quantum systems, statistical image processing, and mathematical frameworks. The book connects theoretical physics with applications in robotics, neural networks, and computer vision. Quantum mechanics with stochastic calculus and filtering theory Radon transform applications in image reconstruction Quantum field theories and chiral anomalies Algebraic geometry and group theory applications Supersymmetry, cohomology, and quantum effective action Suitable for graduate students and researchers in physics, mathematics, and computer science, this book provides theoretical foundations and practical applications for understanding quantum systems and computational implementations. This title has been co-published with Manakin Press. Taylor & Francis does not sell or distribute the print edition in India, Pakistan, Nepal, Bhutan, Sri Lanka and Bangladesh.
A computer is a physical device that helps us process information by executing algorithms. An algorithm is a well-defined procedure, with finite description, for realizing an information-processing task. An information-processing task can always be translated into a physical task. When designing complex algorithms and protocols for various information-processing tasks, it is very helpful, perhaps essential, to work with some idealized computing model. However, when studying the true limitations of a computing device, especially for some practical reason, it is important not to forget the relationship between computing and physics. Real computing devices are embodied in a larger and often richer physical reality than is represented by the idealized computing model. Quantum information processing is the result of using the physical reality that quantum theory tells us about for the purposes of performing tasks that were previously thought impossible or infeasible. Devices that perform quantum information processing are known as quantum computers. In this book we examine how quantum computers can be used to solve certain problems more efficiently than can be done with classical computers, and also how this can be done reliably even when there is a possibility for errors to occur. In this first chapter we present some fundamental notions of computation theory and quantum physics that will form the basis for much of what follows. After this brief introduction, we will review the necessary tools from linear algebra in Chapter 2, and detail the framework of quantum mechanics, as relevant to our model of quantum computation, in Chapter 3. In the remainder of the book we examine quantum teleportation, quantum algorithms and quantum error correction in detail. We are often interested in the amount of resources used by a computer to solve a problem, and we refer to this as the complexity of the computation. An important resource for a computer is time. Another resource is space, which refers to the amount of memory used by the computer in performing the computation. We measure the amount of a resource used in a computation for solving a given problem as a function of the length of the input of an instance of that problem.
Quantum computing (QC) promises to revolutionize data-intensive fields due to its potential for exponential speedups and novel computational paradigms. Satellite image processing (also called Earth Observation, EO) is one such domain, where large volumes of high-dimensional data (e.g., multispectral or hyperspectral images) challenge classical methods in terms of computation, memory, and latency. In this review, we survey the state-of-the-art at the intersection of quantum computing and satellite image processing. We first outline quantum image representations, key quantum algorithms proposed for image processing (e.g., edge detection, segmentation, classification), and quantum machine learning (QML) models. We then examine recent studies that target Earth observation data, including resource estimation and hybrid quantum-classical architectures. Next, we delineate the major challenges (hardware, encoding, noise, scalability) and propose potential solutions, focusing on suitable quantum algorithms and hybrid strategies. Finally, we discuss future directions and research opportunities.