This work introduces a novel quantum algorithm for gradient-based edge detection that operates entirely within the quantum circuit model. Grayscale images are encoded using the Novel Enhanced Quantum Representation (NEQR), allowing exact arithmetic on pixel intensities. Directional gradients are computed by generating superpositions of neighboring pixels via cyclic shift operations and performing subtraction with an exact quantum arithmetic circuit. To refine accuracy, we introduce a direction-aware shifting mechanism that aligns edges with the darker side of intensity transitions. Our novel Quantum Partitioning Algorithm enables efficient in-place thresholding of edge candidates. This work exhibits polynomial-time improvements and optimizes the ancilla count compared to previous NEQR-based quantum edge detection algorithms. These results demonstrate a resource-efficient and fully quantum approach to edge detection, highlighting a practical quantum advantage in image processing.
This work introduces a novel quantum algorithm for gradient-based edge detection that operates entirely within the quantum circuit model. Grayscale images are encoded using the Novel Enhanced Quantum Representation (NEQR), allowing exact arithmetic on pixel intensities. Directional gradients are computed by generating superpositions of neighboring pixels via cyclic shift operations and performing subtraction with an exact quantum arithmetic circuit. To refine accuracy, we introduce a direction-aware shifting mechanism that aligns edges with the darker side of intensity transitions. Our novel Quantum Partitioning Algorithm enables efficient in-place thresholding of edge candidates. This work exhibits polynomial-time improvements and optimizes the ancilla count compared to previous NEQR-based quantum edge detection algorithms. These results demonstrate a resource-efficient and fully quantum approach to edge detection, highlighting a practical quantum advantage in image processing.
Quantum image processing is a system where both quantum computing and image processings are key. Now it is required to investigate how to apply image processing concepts such as image edge detection and improved Sobel operator, to quantum image processing. Just as traditional image processing helps to image manipulation, our efforts in quantum image processing contribute to the further development of quantum algorithm and quantum information theory. Edge detection is an important problem in traditional image processing, and image edge detection algorithm can filter image edge features and retain important attributes. We proposed a quantum edge detection algorithm, which employs improved Sobel operator to improve the performance, so as to solve the problem of unsatisfactory traditional image edge detection methods. In practical application, the amount of image data to be processed increases sharply, and the computing power of classical computer becomes a limitation. Quantum information processing can effectively accelerate many classical problems by virtue of quantum mechanical characteristics, such as quantum superposition, entanglement, parallelism. Our proposed method uses the novel enhanced quantum representation to store quantum images, which stores all the pixels in the image in a superimposed state, realizing parallel computation. The improved eight-direction Sobel operator is used to calculate the gray gradient, and the quantum circuit is designed to realize quantum edge detection. Simulation results have shown that our algorithm can extract edge with dimension 2n× 2nwhen the computational complexity is O(n2+ 2q+3). The improved algorithm can detect more edge details and has strong adaptability.
This paper augments the edge-detecting Sobel Filter with quantum calculations.This implementation replaces the classical stencil operation, which calculates the horizontal and vertical gradients at each pixel through a dot product of edge detection kernels and its neighbors, with a quantum operation that allows each pixel address to gain access to all neighboring values simultaneously.This was done by encoding shifted versions of the original image into a quantum circuit using QCrank and performing the weighted sum calculation within the quantum circuit using the EHands protocol, both developed by LBNL.This thesis also developed a novel reversible version of the EHands parity flip gate.As an additional optimization, this Sobel implementation added functionality to break the input image into tiles on which smaller quantum circuits would act, with their results reassembled into a final image.The experiments of this paper verify the effectiveness of the EHands blocks and gradient calculation circuits, and compare simulation runtime and circuit structure for various combinations of circuit variants and quantum calculations needed for the Sobel Filter.These experiments found the optimal circuit variant for calculating Sobel stencil operations, that GPU nodes outperform CPU nodes at all images sizes, and that divide-and-conquer tiling is superior to processing the input image in its entirety for circuit simulations with larger image sizes.Future work aims to extend the study of this implementation on real quantum hardware and introduce further optimizations to the implementation.v
In this work, a quantum image processing algorithm is developed using the edge extraction method together with the Kirsch operator. In our approach, novel enhanced quantum representation (NEQR) is employed as the image representation model for processing quantum image, which generates results of edge extraction using the Kirsch operator. The proposed algorithm can perform real-time image processing with high accuracy. We carry out the design, analyses, and simulations of quantum circuits based on our approach, which shows that the image processing speed and acuracy are much better than the classical edge extraction algorithms.
Abstract With the increase in the quantity and quality of images that need to be processed, there are higher requirements for the speed of image processing. Thus, it is necessary to speed-up the image processing algorithms. In this paper, we design a quantum image edge detection algorithm based on Laplacian of Gaussian (LoG) operator, which has significant acceleration due to the quantum parallelism. Firstly, the quantum circuit of the novel enhanced quantum image representation (NEQR) model is improved to represent multiple quantum images simultaneously with lower time complexity. Secondly, the quantum subtractor and quantum comparator are optimized. Thirdly, quantum adder, quantum multiplier, quantum comparator, and quantum subtractor are adopted to design the quantum circuit of LoG operator and zero-crossing detector, which constitute the quantum edge detection algorithm. Finally, the algorithm is simulated on the IBM Quantum Experience (IBM Q) platform to verify its feasibility. Compared with the existing state-of-the-art algorithms, the proposed quantum image edge detection algorithm has significant improvement in time complexity.
Image segmentation is a fundamental operation in computer vision, used to partition an image into meaningful regions according to various attributes or spatial relationships among pixels. This paper presents a novel quantum circuit for two-threshold image segmentation implemented in the Novel Enhanced Quantum Representation (NEQR) format. Threshold-based segmentation is particularly attractive for quantum implementation due to its conceptual simplicity and inherent parallelism, but existing approaches suffer from a critical correctness issue: quantum RESET operations are applied to auxiliary qubits that may become entangled with the image data register during the segmentation process. We demonstrate, both theoretically and experimentally, that such RESET operations implicitly perform a measurement that collapses the superposition of the entangled quantum state, leading to non-reproducible segmentation results across repeated executions. To address this limitation, we propose a seven-step quantum segmentation pipeline that explicitly preserves entanglement integrity by allocating dedicated auxiliary qubits for each comparison stage, thereby eliminating unsafe qubit reuse. The proposed circuit encodes pixel intensities using the NEQR representation, applies upper and lower threshold comparators in sequence, and integrates both comparison outcomes to classify pixels into three intensity regions. Experiments conducted on a Qiskit StatevectorSimulator demonstrate that the proposed design produces stable and reproducible segmentation results across all test images, in contrast to the inconsistent behaviour observed in prior work. For images requiring 33 qubits, simulations consumed up to 128 GB of RAM with a total execution time of approximately 27 h on an Intel® Xeon® Gold 6240 CPU @ 2.60 GHz. The proposed approach establishes a robust foundation for quantum image segmentation on future fault-tolerant hardware.
Line detection is a fundamental technique in image processing. It can extract the required information, while the information that does not need attention can be ignored, thus reducing the amount of data. At the same time, line detection is also the basis of image segmentation and plays an important role in this process. In this paper, we implement a quantum algorithm based on a line detection mask for novel enhanced quantum representation (NEQR). We build a quantum algorithm for line detection in different directions and design a quantum circuit for line detection. The detailed module designed is also provided. On a classical computer, we simulate the quantum method, and the simulation results prove the feasibility of the quantum method. By analyzing the complexity of quantum line detection, we find that the computation complexity of the proposed method is improved compared to some similar edge detection algorithms.