This contribution will sum-up the current state of Quantum Computing and give a short overview of the theoretical background. The concept of qubits, superposition, entanglement and measurement of quantum states will be explained. In particular the interaction of multiple quantum objects lead to highly complex combined states, represented by high-dimensional matrices. Quantum gates and circuits built from these gates will be introduced. Next, we will report on the current state of our research on several applications of QC. These include an implementation of cross-sectional image reconstruction for CT imaging based on a QUBO-method (Quadratic Unconstrained Binary Optimization), used to find a mathematical minimum of a metric. Another topic, we are working on, is optimization of X-ray source trajectory in CT with respect to image quality achievable by a restricted number of angular positions. In addition, we evaluate various encoding techniques for images. Based on that we examine common problems of image processing for instance noise reduction and search for ways to implement respective methods on the QC.
This contribution will sum-up the current state of Quantum Computing and give a short overview of the theoretical background. The concept of qubits, superposition, entanglement and measurement of quantum states will be explained. In particular the interaction of multiple quantum objects lead to highly complex combined states, represented by high-dimensional matrices. Quantum gates and circuits built from these gates will be introduced. Next, we will report on the current state of our research on several applications of QC. These include an implementation of cross-sectional image reconstruction for CT imaging based on a QUBO-method (Quadratic Unconstrained Binary Optimization), used to find a mathematical minimum of a metric. Another topic, we are working on, is optimization of X-ray source trajectory in CT with respect to image quality achievable by a restricted number of angular positions. In addition, we evaluate various encoding techniques for images. Based on that we examine common problems of image processing for instance noise reduction and search for ways to implement respective methods on the QC.
The Quantum Image Processing (QIP, based on developing along quantum computing and digital image analysis) is an emerging field. Due to the growing needs for image processing with high speed and efficiency in applications including medical imaging, satellite surveillance, autonomous vehicle, and big data analytics, there are limitations of scalability and speed with classical methods. By exploring the very principles on which the modern world operates, from superposition to entanglement, quantum computing provides an altogether transformative perspective on how to process and analyze images exponentially faster. QIP is the topic of this paper and how it might change the way that images are stored, processed, and interpreted; that is, we look at the theoretical foundations and practical considerations of QIP. Quantum representation of images, quantum Fourier transforms, and quantum edge detection are discussed. The challenges to technical and implementable aspects of QIP need not preclude the next generation of high speed computation. The objectives of this research include discovering the potential of QIP, and the weaknesses and future occurrence of the QIP technology.
A new paradigm of computation has appeared recently that is known as quantum computing and which is able to solve difficult problems that cannot be solved using classical computing.It provides a comprehensive overview of the basic concepts, computational models, architectures, tools, applications and implications of quantum computing.The paper is based on the latest research, and it's used to evaluate the state of quantum computing, from theory to practical, application-oriented implementations.Fundamental concepts such as qubits, superposition, entanglement and hybrid quantum-classical computation are explained, along with major models and frameworks of quantum computing, including Cirq, TensorFlow Quantum and ProjectQ.Furthermore, various significant application areas, such as cryptography, optimization, AI, drug discovery, and industrial computing, are explored, highlighting the applications' potential and their technological development.Although great strides have been made, scalability, noise, error correction, interoperability and hardware challenges are major problems in the NISQ era.It also examines new trends, future research directions and the societal, security and ethical considerations of the advancement of quantum technology.But the bigger picture indicates that quantum computing is progressing from theory to reality, and there are many technical and institutional challenges to be resolved along the way.Interdisciplinary studies and responsible innovation will be key to unlocking the potential of quantum computing as the next generation of computational technology.
Quantum Computing (QC) technology has made tremendous progress recently. Today, first real QC systems are operational. The Fraunhofer Institute of Integrated Circuits IIS, Division Development Center X-ray Technology is participating in the project BayQS, which aims at identifying and evaluating potential applications of quantum computing in the field of non-destructive testing by means of X-ray imaging. Computed tomography (CT) is a well-established method of 3-D imaging in medicine and industry since several decades. The quality of the resulting volumetric datasets depends essentially on a set of around 20 physical parameters controlling the set-up of X-ray source, X-ray digital detector array, movable mechanical components, sample rotation, and image processing. Hence, we started investigating the potential use of a quantum-based optimization method to optimize CT data acquisition. As a first approach, we focused on the task to minimize the number of projection images required to achieve unaltered image quality when compared to an acquisition over the full 4π solid angle. Minimizing involves a cost function. There are several ways to define this cost function. In order to transfer the task to a QC system, the problem is reformulated as a Quadratic Unconstrained Binary Optimization (QUBO). A decisive feature of the QUBO is, that is can be solved by a hybrid implementation on quantum devices, which combines a classical part to iteratively find the maximum of the cost function and a quantum circuit to evaluate the respective cost function for each combination of projections. This procedure is called quantum approximate optimization algorithm (QAOA). The experiments were performed via the qiskit script language on an IBM Quantum System One located in Ehingen, Germany. This QC system offers 27 qubits and enables first real world experiments with small test datasets.
Quantum computing, with its promise of exponential speedups and novel algorithms, has emerged as a revolutionary technology in various computational fields, including image processing. This survey provides a comprehensive review of the applications of quantum computing in image processing, exploring how quantum algorithms and hardware can address classical challenges such as image compression, enhancement, pattern recognition, and image recovery. The paper delves into the theoretical foundations of quantum computing, discusses current advancements, and compares quantum methods with traditional approaches. Moreover, it identifies the key challenges, such as scalability and hardware limitations, that currently hinder the widespread adoption of quantum techniques in image processing. Finally, the survey outlines future research directions, highlighting the potential for further integration of quantum computing with advanced image processing technologies like deep learning. This work aims to serve as a foundational reference for researchers and practitioners interested in the intersection of quantum computing and image processing.
One of the primary difficulties in computed tomography (CT) is reconstructing cross-sectional images from measured projections of a physical object. There exist several classical methods for this task of generating a digital representation of the object, including filtered backprojection or simultaneous algebraic reconstruction technique. Our research aims to explore the potential of quantum computing in the field of industrial X-ray transmission tomography. Specifically, this work focuses on the application of a method similar to that proposed by Nau et al. (2023) on real CT data to demonstrate the feasibility of quadratic-unconstrained-binary-optimization-based tomographic reconstruction. Starting with simulated phantoms, results with simulated annealing as well as real annealing hardware are shown, leading to the application on measured cone-beam CT data. The results demonstrate that tomographic reconstruction using quantum annealing is feasible for both simulated and real-world applications. Yet, current limitations—involving the maximum processable size and bit depth of voxel values of the images, both correlated with the number of densely connected qubits within the annealing hardware—imply the need of future research to further improve the results. This approach, despite its early stage, has the potential to enable more sophisticated reconstructions, providing an alternative to traditional classical methods.
Image processing on quantum platform is a hot topic for researchers now a day. Inspired from the idea of quantum physics, researchers are trying to shift their focus from classical image processing towards quantum image processing.This paper starts with a brief review of the principles which underlie quantum computing, and also deals with some of the basics of qubits and quantum computing.Then this paper starts to deal with some different methods of image storage, representation and retrieval in a quantum system.This paper also describes the advantages of using those methods in quantum systems compare to classical systems. In the next section, a short discussion on some of the important aspects, comparison among them and applications of quantum image processing is presented. A few other hot topics and open problems in quantum image processing are mentioned in this paper. This review article will provide the readership an overview of progress witnessed in the area of Quantum Image processing while also simulating further interest to pursue more advanced research in it.