A hybrid quantum image edge detector for the NISQ era
- URL: http://arxiv.org/abs/2203.12072v1
- Date: Tue, 22 Mar 2022 22:02:09 GMT
- Title: A hybrid quantum image edge detector for the NISQ era
- Authors: Alexander Geng, Ali Moghiseh, Claudia Redenbach, Katja Schladitz
- Abstract summary: 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.
- Score: 62.997667081978825
- License: http://creativecommons.org/licenses/by/4.0/
- 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.
Related papers
- 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) - Deep learning as a tool for quantum error reduction in quantum image
processing [0.0]
We report the successful use of a generative adversarial network trained for image-to-image translation, in conjunction with Phase Unraveling error reduction method, for reducing overall error in images encoded using LPIQE.
Despite the limited availability and quantum volume of quantum computers, quantum image representation is a widely researched area.
arXiv Detail & Related papers (2023-11-08T10:14:50Z) - Tensor Network Based Efficient Quantum Data Loading of Images [0.0]
We present a novel method for creating quantum states that approximately encode images as amplitudes.
We experimentally demonstrate our technique on 8 qubits of a trapped ion quantum computer for complex images of road scenes.
arXiv Detail & Related papers (2023-10-09T17:40:41Z) - 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) - Block-wise quantum grayscale image representation and compression scheme
using state connection [9.653976364051564]
A novel SCMNEQR approach has been proposed that uses fewer qubits to map the arbitrary size of the grayscale image.
The experimental results show that the proposed approach outperforms the existing methods in terms of compression.
arXiv Detail & Related papers (2022-12-19T03:17:53Z) - Entanglement and coherence in Bernstein-Vazirani algorithm [58.720142291102135]
Bernstein-Vazirani algorithm allows one to determine a bit string encoded into an oracle.
We analyze in detail the quantum resources in the Bernstein-Vazirani algorithm.
We show that in the absence of entanglement, the performance of the algorithm is directly related to the amount of quantum coherence in the initial state.
arXiv Detail & Related papers (2022-05-26T20:32:36Z) - Improved FRQI on superconducting processors and its restrictions in the
NISQ era [62.997667081978825]
We study the feasibility of the Flexible Representation of Quantum Images (FRQI)
We also check experimentally what is the limit in the current noisy intermediate-scale quantum era.
We propose a method for simplifying the circuits needed for the FRQI.
arXiv Detail & Related papers (2021-10-29T10:42:43Z) - Advantages and Bottlenecks of Quantum Machine Learning for Remote
Sensing [63.69764116066747]
This concept paper aims to provide a brief outline of quantum computers, explore existing methods of quantum image classification techniques, and discuss the bottlenecks of performing these algorithms on currently available open source platforms.
Next steps include expanding the size of the quantum hidden layer and increasing the variety of output image options.
arXiv Detail & Related papers (2021-01-26T09:31:46Z) - Quantum walk processes in quantum devices [55.41644538483948]
We study how to represent quantum walk on a graph as a quantum circuit.
Our approach paves way for the efficient implementation of quantum walks algorithms on quantum computers.
arXiv Detail & Related papers (2020-12-28T18:04:16Z)
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.