Zobrazeno 1 - 10
of 3 898
pro vyhledávání: '"ʻUsmān, Muḥammad"'
Convolutional Neural Networks (CNNs) are crucial in various applications, but their deployment on resource-constrained edge devices poses challenges. This study presents the Sum-of-Products (SOP) units for convolution, which utilize low-latency left-
Externí odkaz:
http://arxiv.org/abs/2412.13724
Autor:
Rehman, Abd Ur, Rehman, Azka, Usman, Muhammad, Shahid, Abdullah, Gho, Sung-Min, Lee, Aleum, Khan, Tariq M., Razzak, Imran
Brain aging involves structural and functional changes and therefore serves as a key biomarker for brain health. Combining structural magnetic resonance imaging (sMRI) and functional magnetic resonance imaging (fMRI) has the potential to improve brai
Externí odkaz:
http://arxiv.org/abs/2412.05632
The surface code family is a promising approach to implementing fault-tolerant quantum computations. Universal fault-tolerance requires error-corrected non-Clifford operations, in addition to Clifford gates, and for the former, it is imperative to ex
Externí odkaz:
http://arxiv.org/abs/2412.01446
Autor:
Nakhl, Azar C., Harper, Ben, West, Maxwell, Dowling, Neil, Sevior, Martin, Quella, Thomas, Usman, Muhammad
This work augments the recently introduced Stabilizer Tensor Network (STN) protocol with magic state injection, reporting a new framework with significantly enhanced ability to simulate circuits with an extensive number of non-Clifford operations. Sp
Externí odkaz:
http://arxiv.org/abs/2411.12482
Autor:
Usman, Muhammad, Rehman, Azka, Shahid, Abdullah, Rehman, Abd Ur, Gho, Sung-Min, Lee, Aleum, Khan, Tariq M., Razzak, Imran
Despite advances in deep learning for estimating brain age from structural MRI data, incorporating functional MRI data is challenging due to its complex structure and the noisy nature of functional connectivity measurements. To address this, we prese
Externí odkaz:
http://arxiv.org/abs/2411.10100
Autor:
Klymenko, Mykhailo, Hoang, Thong, Xu, Xiwei, Xing, Zhenchang, Usman, Muhammad, Lu, Qinghua, Zhu, Liming
Utilising quantum computing technology to enhance artificial intelligence systems is expected to improve training and inference times, increase robustness against noise and adversarial attacks, and reduce the number of parameters without compromising
Externí odkaz:
http://arxiv.org/abs/2411.10487
The realization of fault-tolerant quantum computers hinges on effective quantum error correction protocols, whose performance significantly relies on the nature of the underlying noise. In this work, we directly study the structure of non-Markovian c
Externí odkaz:
http://arxiv.org/abs/2410.23779
Autor:
Wang, Zeheng, Wang, Fangzhou, Li, Liang, Wang, Zirui, van der Laan, Timothy, Leon, Ross C. C., Huang, Jing-Kai, Usman, Muhammad
This paper pioneers the use of quantum machine learning (QML) for modeling the Ohmic contact process in GaN high-electron-mobility transistors (HEMTs) for the first time. Utilizing data from 159 devices and variational auto-encoder-based augmentation
Externí odkaz:
http://arxiv.org/abs/2409.10803
The no-reference image quality assessment is a challenging domain that addresses estimating image quality without the original reference. We introduce an improved mechanism to extract local and non-local information from images via different transfor
Externí odkaz:
http://arxiv.org/abs/2409.07115
The rapid growth of Internet of Things (IoT) devices necessitates efficient data compression techniques to handle the vast amounts of data generated by these devices. Chemiresistive sensor arrays (CSAs), a simple-to-fabricate but crucial component in
Externí odkaz:
http://arxiv.org/abs/2409.00115