Abstract The size and number of images and the amount of data we process every day have grown rapidly over the last years. Quantum computers promise to process this data more efficiently since classical images can be stored in quantum states. Experiments on quantum computer simulators prove the paradigms this promise is built on to be correct. However, currently, running the very same algorithms on a real quantum computer is often too error-prone to be of any practical use. We explore the current possibilities for image processing on real quantum computers. We redesign a commonly used quantum image encoding technique to reduce its susceptibility to errors. We show experimentally that the current size limit for images to be encoded on a quantum computer and subsequently retrieved with an error of at most 5 % is 2 × 2 pixels. A way to circumvent this limitation is to combine ideas of classical filtering with a quantum algorithm operating locally, only. We show the practicability of this strategy using the application example of edge detection. Our hybrid filtering scheme’s quantum part is an artificial neuron, working well on real quantum computers, too.
Abstract The size and number of images and the amount of data we process every day have grown rapidly over the last years. Quantum computers promise to process this data more efficiently since classical images can be stored in quantum states. Experiments on quantum computer simulators prove the paradigms this promise is built on to be correct. However, currently, running the very same algorithms on a real quantum computer is often too error-prone to be of any practical use. We explore the current possibilities for image processing on real quantum computers. We redesign a commonly used quantum image encoding technique to reduce its susceptibility to errors. We show experimentally that the current size limit for images to be encoded on a quantum computer and subsequently retrieved with an error of at most 5 % is 2 × 2 pixels. A way to circumvent this limitation is to combine ideas of classical filtering with a quantum algorithm operating locally, only. We show the practicability of this strategy using the application example of edge detection. Our hybrid filtering scheme’s quantum part is an artificial neuron, working well on real quantum computers, too.
The size of images and data we process every day have been growing exponentially over the last years. Quantum computers promise to process this data more efficiently. Experiments on quantum computer simulators prove the paradigms this promise is built on to be correct. However, currently, running the very same algorithms on a real quantum computer is often too error prone to be of any practical use. We explore the current possibilities for image processing on real quantum computers. We redesign a commonly used quantum image encoding technique to reduce its susceptibility to errors. We show experimentally that the current size limit for images to be encoded on the quantum computer and subsequently retrieved with an error of at most 5% is 2×2 pixels. A way to circumvent this limitation is to combine ideas of classical filtering with a quantum algorithm operating locally, only. We show the practicability of this strategy using the application example of edge detection. Our hybrid filtering scheme’s quantum part is an artificial neuron, working well on real quantum computers, too.
Abstract Edges are image locations where the gray value intensity changes suddenly. They are among the most important features to understand and segment an image. Edge detection is a standard task in digital image processing, solved, for example, using filtering techniques. However, the amount of data to be processed grows rapidly and pushes even supercomputers to their limits. Quantum computing promises exponentially lower memory usage in terms of the number of qubits compared to the number of classical bits. In this paper, we propose a hybrid method for quantum edge detection based on the idea of a quantum artificial neuron. Our method can be practically implemented on quantum computers, especially on those of the current noisy intermediate-scale quantum era. We compare six variants of the method to reduce the number of circuits and thus the time required for the quantum edge detection. Taking advantage of the scalability of our method, we can practically detect edges in images considerably larger than reached before.
Abstract In image processing, the amount of data to be processed grows rapidly, in particular when dealing with images of more than two dimensions or time series of images. Thus, efficient processing is a challenge, as data sizes may push even supercomputers to their limits. Quantum image processing promises to encode images with logarithmically less qubits than classical pixels in the image. In theory, this is a huge progress, but so far not many experiments have been conducted in practice, in particular on real backends. Often, the precise conversion of classical data to quantum states, the exact implementation, and the interpretation of the measurements in the classical context are challenging. We investigate these practical questions in this paper. In particular, we study the feasibility of the flexible representation of quantum images (FRQI). Furthermore, we check experimentally the limit in the current noisy intermediate-scale quantum era, i.e., up to which image size an image can be encoded, both on simulators and on real backends. Finally, we propose a method for simplifying the circuits needed for the FRQI. With our alteration, the number of gates can be reduced, especially the one of the error-prone controlled-NOT gates. As a consequence, the size of manageable images increases.
In image processing, the amount of data to be processed grows rapidly, in particular when imaging methods yield images of more than two dimensions or time series of images. Thus, efficient processing is a challenge, as data sizes may push even supercomputers to their limits. Quantum image processing promises to encode images with logarithmically less qubits than classical pixels in the image. In theory, this is a huge progress, but so far not many experiments have been conducted in practice, in particular on real backends. Often, the precise conversion of classical data to quantum states, the exact implementation, and the interpretation of the measurements in the classical context are challenging. We investigate these practical questions in this paper. In particular, we study the feasibility of the Flexible Representation of Quantum Images (FRQI). Furthermore, we check experimentally what is the limit in the current noisy intermediate-scale quantum era, i.e. up to which image size an image can be encoded, both on simulators and on real backends. Finally, we propose a method for simplifying the circuits needed for the FRQI. With our alteration, the number of gates needed, especially of the error-prone controlled-NOT gates, can be reduced. As a consequence, the size of manageable images increases.
Quantum algorithms have emerged as a transformative approach to address the computational challenges of high-dimensional image processing tasks. This research investigates the integration of quantum computing principles, such as Quantum Fourier Transform (QFT) and Quantum Principal Component Analysis (QPCA), to optimize processes like edge detection, noise reduction, and image segmentation. These quantum-enhanced techniques promise significant improvements in computational efficiency and accuracy compared to classical methods, especially when handling complex visual data in real-time applications like medical imaging, satellite imagery, and autonomous systems. Additionally, the study explores hybrid quantum-classical architectures to bridge current limitations in noisy intermediate-scale quantum (NISQ) devices, ensuring resource optimization and scalability. The potential of quantum algorithms to provide faster, more precise solutions is evaluated alongside challenges like error mitigation and algorithmic stability. This research highlights the future role of quantum computing in revolutionizing image processing, paving the way for breakthroughs in computer vision and artificial intelligence.
Quantum Image Processing (QIP)is an exciting new field showing a lot of promise as a powerful addition to the arsenal of Image Processing techniques. Representing image pixel by pixel using classical information requires an enormous amount of computational resources. Hence, exploring methods to represent images in a different paradigm of information is important. In this work, we study the representation of images in Quantum Information. The main motivation for this pursuit is the ability of storing N bits of classical information in only log(2N) quantum bits (qubits). The promising first step was the exponentially efficient implementation of the Fourier transform in quantum computers as compared to Fast Fourier Transform in classical computers. In addition, images encoded in quantum information could obey unique quantum properties like superposition or entanglement.