Grover's algorithm for image edge detection
- URL: http://arxiv.org/abs/2607.15744v1
- Date: Fri, 17 Jul 2026 08:29:01 GMT
- Title: Grover's algorithm for image edge detection
- Abstract summary: We use Groves's algorithm to detect the edges of a digital image faster than any classical edge detection methods.<n>The high success probability of our method in detecting image edges, compared to most existing quantum edge detection methods, makes it a more suitable candidate for use in the presence of noise on current quantum devices.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Grover's algorithm is one of the most significant quantum algorithms, enabling the search for a marked element in an unsorted database quadratically faster than classical search algorithms. We utilize Groves's algorithm to detect the edges of a digital image faster than any classical edge detection methods. The high success probability of our method in detecting image edges, compared to most existing quantum edge detection methods, makes it a more suitable candidate for use in the presence of noise on current quantum devices. We implemented our model for the $2\times 2$ blocks of the image using a two-qubit Qiskit circuit with the QASM simulator and the noisy IBM Sydney(fake) device, making it suitable for most of the current NISQ devices.
Related papers
- Quantum walk search based edge detection of images [1.6822770693792821]
We propose a novel application of this advanced quantum walk search algorithm for the edge detection of imagestextemdash.<n>Our quantum walk search algorithm demonstrates a high success probability in detecting the image edges.<n>A small Qiskit circuit implementation of our method using a one-dimensional quantum walk search has been executed.
arXiv Detail & Related papers (2025-10-06T01:15:18Z) - 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) - On the practicality of quantum sieving algorithms for the shortest vector problem [42.70026220176376]
lattice-based cryptography is one of the main candidates of post-quantum cryptography.<n> cryptographic security against quantum attackers is based on lattice problems like the shortest vector problem (SVP)<n>Asymptotic quantum speedups for solving SVP are known and rely on Grover's search.
arXiv Detail & Related papers (2024-10-17T16:54:41Z) - 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 Block-Matching Algorithm using Dissimilarity Measure [0.0]
Finding groups of similar image blocks within an ample search area is often necessary in different applications.
A block-matching algorithm that uses a dissimilarity measure can be applied in such scenarios.
A measure that utilizes the quantum Fourier transform or the Swap test based on the Euclidean distance is proposed.
arXiv Detail & Related papers (2023-09-27T17:11:22Z) - Hybrid quantum transfer learning for crack image classification on NISQ
hardware [62.997667081978825]
We present an application of quantum transfer learning for detecting cracks in gray value images.
We compare the performance and training time of PennyLane's standard qubits with IBM's qasm_simulator and real backends.
arXiv Detail & Related papers (2023-07-31T14:45:29Z) - An Unforgeable Publicly Verifiable Watermark for Large Language Models [84.2805275589553]
Current watermark detection algorithms require the secret key used in the watermark generation process, making them susceptible to security breaches and counterfeiting during public detection.
We propose an unforgeable publicly verifiable watermark algorithm named UPV that uses two different neural networks for watermark generation and detection, instead of using the same key at both stages.
arXiv Detail & Related papers (2023-07-30T13:43:27Z) - Scalable noisy quantum circuits for biased-noise qubits [37.69303106863453]
We consider biased-noise qubits affected only by bit-flip errors, which is motivated by existing systems of stabilized cat qubits.
For realistic noise models, phase-flip will not be negligible, but in the Pauli-Twirling approximation, we show that our benchmark could check the correctness of circuits containing up to $106$ gates.
arXiv Detail & Related papers (2023-05-03T11:27:50Z) - An efficient quantum-classical hybrid algorithm for distorted
alphanumeric character identification [0.0]
The proposed algorithm can transform a low-resolution bitonal image of a character from the set of alphanumeric characters into a high-resolution image.
The quantum part of the proposed algorithm fruitfully utilizes a variant of Grover's search algorithm, known as the fixed point search algorithm.
arXiv Detail & Related papers (2022-12-25T05:31:51Z) - 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) - Deep Reinforcement Learning with Label Embedding Reward for Supervised
Image Hashing [85.84690941656528]
We introduce a novel decision-making approach for deep supervised hashing.
We learn a deep Q-network with a novel label embedding reward defined by Bose-Chaudhuri-Hocquenghem codes.
Our approach outperforms state-of-the-art supervised hashing methods under various code lengths.
arXiv Detail & Related papers (2020-08-10T09:17:20Z)
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.