Zobrazeno 1 - 10
of 336
pro vyhledávání: '"QML"'
Publikováno v:
Discover Applied Sciences, Vol 6, Iss 10, Pp 1-12 (2024)
Abstract Quantum machine learning (QML) algorithms have demonstrated the power of quantum computing for solving complex problems and big data in certain tasks. In this study, we explore the capabilities of QML for the classification of real-world bio
Externí odkaz:
https://doaj.org/article/c4083d5b209b4c3f84e7214ffc1002ee
Publikováno v:
IEEE Access, Vol 12, Pp 169671-169682 (2024)
Credit card fraud detection is crucial for financial security which entails identifying unauthorized transactions that can result in significant financial losses. Detection is inherently challenging due to the rarity and indistinguishability of fraud
Externí odkaz:
https://doaj.org/article/cc52584f1e7645e89b464d84a5219d9b
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 17, Pp 12477-12489 (2024)
Exploiting machine learning techniques to automatically classify multispectral remote sensing imagery plays a significant role in deriving changes on the Earth’s surface. However, the computation power required to manage large Earth observation dat
Externí odkaz:
https://doaj.org/article/6d1c83669ff54ad4a593a48073f9747a
Autor:
Matvei Anoshin, Asel Sagingalieva, Christopher Mansell, Dmitry Zhiganov, Vishal Shete, Markus Pflitsch, Alexey Melnikov
Publikováno v:
IEEE Transactions on Quantum Engineering, Vol 5, Pp 1-14 (2024)
The drug design process currently requires considerable time and resources to develop each new compound that enters the market. This work develops an application of hybrid quantum generative models based on the integration of parameterized quantum ci
Externí odkaz:
https://doaj.org/article/befb5560bf3b49f8b5b0fcb555027d50
Publikováno v:
IEEE Transactions on Quantum Engineering, Vol 5, Pp 1-14 (2024)
Quanvolutional neural networks (QNNs) have been successful in image classification, exploiting inherent quantum capabilities to improve performance of traditional convolution. Unfortunately, the qubit's reliability can be a significant issue for QNNs
Externí odkaz:
https://doaj.org/article/0f6faecd1e364bfb84c95754fe533251
Publikováno v:
IEEE Transactions on Quantum Engineering, Vol 5, Pp 1-9 (2024)
This article examines the current status of quantum computing (QC) in Earth observation and satellite imagery. We analyze the potential limitations and applications of quantum learning models when dealing with satellite data, considering the persiste
Externí odkaz:
https://doaj.org/article/2925ac677de245b488ac4e7d0f8b520d
Autor:
Deepak Ranga, Aryan Rana, Sunil Prajapat, Pankaj Kumar, Kranti Kumar, Athanasios V. Vasilakos
Publikováno v:
Mathematics, Vol 12, Iss 21, p 3318 (2024)
Quantum computing and machine learning (ML) have received significant developments which have set the stage for the next frontier of creative work and usefulness. This paper aims at reviewing various data-encoding techniques in Quantum Machine Learni
Externí odkaz:
https://doaj.org/article/20b94082377442869c2702ce6b7d16d3
Autor:
Olaoye, Olumide Olusegun
Publikováno v:
Journal of Economic Studies, 2022, Vol. 50, Issue 3, pp. 480-505.
Externí odkaz:
http://www.emeraldinsight.com/doi/10.1108/JES-07-2021-0322
Autor:
Sharanya Prabhu, Shourya Gupta, Gautham Manuru Prabhu, Aarushi Vishal Dhanuka, K. Vivekananda Bhat
Publikováno v:
IEEE Access, Vol 11, Pp 136122-136135 (2023)
This research is the first of its kind to leverage the power of Quantum Machine Learning (QML) to perform multi-class classification of Cardiovascular Diseases (CVDs). We propose a novel approach that enables multi-class classification with Pegasos Q
Externí odkaz:
https://doaj.org/article/ce9d14a95e064879870a37407aae4df5
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 16, Pp 9223-9230 (2023)
Quantum machine learning (QML) models promise to have some computational (or quantum) advantage for classifying supervised datasets (e.g., satellite images) over some conventional deep learning (DL) techniques due to their expressive power via their
Externí odkaz:
https://doaj.org/article/27896af76fe745ea9fbe1964a4c8ed67