Dwi Ratna Sulistyaningrum

Work place: Department of Mathematics, Institute Teknologi Sepuluh Nopember, Surabaya, Indonesia

E-mail: dwratna@gmail.com

Website: https://orcid.org/0000-0001-5876-8205

Research Interests:

Biography

Dwi Ratna Sulistyaningrum graduated with a bachelor's degree in mathematics in 1993 and a master's in informatics engineering in 1998. She obtained a bachelor's degree in mathematics in 1993 and a master's in informatics engineering in 1998 from the Institut Teknologi Bandung. Her thesis was titled "Optical Flow Estimation Using Spatiotemporal Methods." In 2007, she began her doctoral education at the Department of Electrical Engineering at Institut Teknologi Sepuluh Nopember Indonesia, where she completed her dissertation titled "Stabilization of Ancient Animation Videos Using Motion Estimation Based on Wavelet Transformation and Inbetweening." Since 1994, she has been a lecturer in the Mathematics Department at the Institut Teknologi Sepuluh Nopember, where she teaches and conducts research. Her area of expertise is image and video processing, and she has authored several papers on this topic in national and international journals. Currently, her research focuses on identifying types of asphalt road damage and plant diseases based on image and video data, using machine learning and deep learning techniques. Additionally, she is interested in researching machine learning methods based on quantum theory that aligns with the development of quantum theory.

Author Articles
In-depth Study of Quantum Hadamard Gate Edge Detection: Complexity Analysis, Experiments, and Future Directions

By Ridho Nur Rohman Wijaya Budi Setiyono Dwi Ratna Sulistyaningrum

DOI: https://doi.org/10.5815/ijigsp.2025.05.02, Pub. Date: 8 Oct. 2025

Quantum computing is a rapidly developing field with faster computational capabilities than classical computing. The popularity of quantum computing has reached the field of image processing, particularly with a breakthrough method known as Quantum Hadamard Edge Detection. This approach represents a significant advancement in edge detection techniques using quantum computing. Quantum Hadamard Edge Detection is a method that can detect image edges more quickly than classical methods with exponential acceleration. This paper explains the Quantum Hadamard Edge Detection method in detail, including how it is implemented, a time complexity explanation, some experiments, and future research directions. Our experiments utilize a quantum computer simulator and employ four measurement metrics: Structural Similarity Index, Figure of Merit, Entropy, and a Proposed Metric with radius-based features, to detect simple binary images, MNIST images, and the Berkeley Segmentation datasets. We recognize the potential of quantum computing and believe that image processing with quantum representation will make processing more efficient and significantly valuable in the future.

[...] Read more.
Other Articles