A Fully Quantum Algorithm for Image Edge Detection

• URL: http://arxiv.org/abs/2604.23535v2
• Date: Tue, 28 Apr 2026 19:13:33 GMT
• Title: A Fully Quantum Algorithm for Image Edge Detection
• Abstract summary: This work introduces a novel quantum gradient-based edge detection that operates entirely within the quantum circuit model.<n>We introduce a direction-aware shifting mechanism that aligns edges with the darker side of intensity transitions.<n>Results demonstrate a resource-efficient and fully quantum approach to edge detection, highlighting a practical quantum advantage in image processing.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

Related papers

• Balancing Expressivity and Learnability in Quantum Kernel Bandit Optimization [50.880384999888044]
  We develop misspecified GP bandit algorithms and derive regret bounds that characterize the trade-off between approximation error and information gain.<n>Our methods outperform full quantum kernels in sample efficiency, while substantially reducing computational overhead.
  arXiv  Detail & Related papers  (2026-07-01T15:38:00Z)
• Quantum Gradient-Based Approach for Edge and Corner Detection Using Sobel Kernels [1.0260880679794957]
  Edge detection refers to identifying points in a digital image where intensity changes sharply, indicating object boundaries or structural features.<n>We present a quantum implementation of Sobel-based edge detection and Harris-style corner detection.
  arXiv  Detail & Related papers  (2026-05-01T15:48:20Z)
• Quantum algorithm for edge detection in digital grayscale images [0.0]
  We propose a novel quantum algorithm for edge detection in digital grayscale images, based on the sequency-ordered Walsh-Hadamard transform.<n>The proposed method significantly improves upon existing quantum techniques for edge detection by using a quantum algorithm for the sequency-ordered Walsh-Hadamard transform.<n>Our approach for edge detection has a computational cost (both gate complexity and quantum circuit depth) of $mathcalO(log_2(N_1N_2))$ for an image of size $N_1times N
  arXiv  Detail & Related papers  (2025-07-09T08:14:37Z)
• A Quantum Range-Doppler Algorithm for Synthetic Aperture Radar Image Formation [47.1482540145286]
  We show how in general reference functions, a key element in many SAR focusing algorithms, can be mapped to quantum gates.<n>We find that the core of the quantum range-Doppler algorithm has a computational complexity $O(N)$, less than its classical counterpart.
  arXiv  Detail & Related papers  (2025-04-29T14:24:23Z)
• Fast Expectation Value Calculation Speedup of Quantum Approximate Optimization Algorithm: HoLCUs QAOA [55.2480439325792]
  We present a new method for calculating expectation values of operators that can be expressed as a linear combination of unitary (LCU) operators.<n>This method is general for any quantum algorithm and is of particular interest in the acceleration of variational quantum algorithms.
  arXiv  Detail & Related papers  (2025-03-03T17:15:23Z)
• A Photonic Parameter-shift Rule: Enabling Gradient Computation for Photonic Quantum Computers [0.0]
  We present a method for gradient computation in quantum computation algorithms implemented on linear optical quantum computing platforms.<n>Our method scales linearly with the number of input photons and utilizes the same parameterized photonic circuit with shifted parameters for each evaluation.
  arXiv  Detail & Related papers  (2024-10-03T17:47:38Z)
• Edge Detection Quantumized: A Novel Quantum Algorithm For Image Processing [0.0]
  This paper presents a novel protocol by combining the Flexible Representation of Quantum Images (FRQI) encoding and a modified QHED algorithm. An improved edge outline method has been proposed in this work resulting in a better object outline output and more accurate edge detection than the traditional QHED algorithm.
  arXiv  Detail & Related papers  (2024-04-10T10:29:08Z)
• Quantum Subroutine for Variance Estimation: Algorithmic Design and Applications [80.04533958880862]
  Quantum computing sets the foundation for new ways of designing algorithms. New challenges arise concerning which field quantum speedup can be achieved. Looking for the design of quantum subroutines that are more efficient than their classical counterpart poses solid pillars to new powerful quantum algorithms.
  arXiv  Detail & Related papers  (2024-02-26T09:32:07Z)
• A quantum segmentation algorithm based on local adaptive threshold for NEQR image [7.798738743268923]
  The complexity of our algorithm can be reduced to $O(n2+q)$, which is an exponential speedup compared to the classic counterparts. The experiment is conducted on IBM Q to show the feasibility of our algorithm in the noisy intermediate-scale quantum (NISQ) era.
  arXiv  Detail & Related papers  (2023-10-02T04:01:42Z)
• Quantum Annealing for Single Image Super-Resolution [86.69338893753886]
  We propose a quantum computing-based algorithm to solve the single image super-resolution (SISR) problem. The proposed AQC-based algorithm is demonstrated to achieve improved speed-up over a classical analog while maintaining comparable SISR accuracy.
  arXiv  Detail & Related papers  (2023-04-18T11:57:15Z)
• Automatic and effective discovery of quantum kernels [41.61572387137452]
  Quantum computing can empower machine learning models by enabling kernel machines to leverage quantum kernels for representing similarity measures between data.<n>We present an approach to this problem, which employs optimization techniques, similar to those used in neural architecture search and AutoML.<n>The results obtained by testing our approach on a high-energy physics problem demonstrate that, in the best-case scenario, we can either match or improve testing accuracy with respect to the manual design approach.
  arXiv  Detail & Related papers  (2022-09-22T16:42:14Z)
• A hybrid quantum image edge detector for the NISQ era [62.997667081978825]
  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.
  arXiv  Detail & Related papers  (2022-03-22T22:02:09Z)
• Variational Quantum Optimization with Multi-Basis Encodings [62.72309460291971]
  We introduce a new variational quantum algorithm that benefits from two innovations: multi-basis graph complexity and nonlinear activation functions. Our results in increased optimization performance, two increase in effective landscapes and a reduction in measurement progress.
  arXiv  Detail & Related papers  (2021-06-24T20:16:02Z)
• A Quantum Edge Detection Algorithm [2.639737913330821]
  We show how, by taking advantage of quantum properties like entanglement and superposition, many image processing algorithms could have an exponential speed-up. We propose an improved version of a quantum edge detection algorithm.
  arXiv  Detail & Related papers  (2020-12-20T22:10:05Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.