Zobrazeno 1 - 10
of 416
pro vyhledávání: '"Quantum neural networks"'
Publikováno v:
Alexandria Engineering Journal, Vol 109, Iss , Pp 807-819 (2024)
Identification of nonlinear dynamic systems is a critical task in various fields. Artificial neural networks have been widely used for this purpose due to their ability to approximate complex functions. However, their computational efficiency and sta
Externí odkaz:
https://doaj.org/article/97a0062f6ab14f18b93d5af49711ae6b
Autor:
Soohyun Park, Joongheon Kim
Publikováno v:
ETRI Journal, Vol 46, Iss 5, Pp 748-758 (2024)
This paper presents the basic quantum reinforcement learning theory and its applications to various engineering problems. With the advances in quantum computing and deep learning technologies, various research works have focused on quantum deep learn
Externí odkaz:
https://doaj.org/article/7b00199bdd254214af7c75562dd52391
Autor:
Saad Zafar Khan, Nazeefa Muzammil, Salman Ghafoor, Haibat Khan, Syed Mohammad Hasan Zaidi, Abdulah Jeza Aljohani, Imran Aziz
Publikováno v:
Frontiers in Physics, Vol 12 (2024)
Accurate solar power forecasting is pivotal for the global transition towards sustainable energy systems. This study conducts a meticulous comparison between Quantum Long Short-Term Memory (QLSTM) and classical Long Short-Term Memory (LSTM) models fo
Externí odkaz:
https://doaj.org/article/80bd2cfad1a54b6aa2216bab5f6267a0
Publikováno v:
Ain Shams Engineering Journal, Vol 15, Iss 8, Pp 102851- (2024)
This paper presents an identification model based on quantum neural network for engineering systems. Quantum neural network (QNN) is a superior strategy to improve the computational efficiency for conventional neural network structures due to their u
Externí odkaz:
https://doaj.org/article/43feeaaacb0f470bbce4c519fccc4e70
Publikováno v:
IEEE Transactions on Quantum Engineering, Vol 5, Pp 1-19 (2024)
As fifth-generation (5G) and next-generation communication systems advance and find widespread application in critical infrastructures, the importance of vulnerability detection becomes increasingly critical. The growing complexity of these systems n
Externí odkaz:
https://doaj.org/article/ce49dc3e14a84962be843723c77bb792
Autor:
Jinkai Tian, Wenjing Yang
Publikováno v:
Entropy, Vol 26, Iss 11, p 987 (2024)
Quantum generative models have shown promise in fields such as quantum chemistry, materials science, and optimization. However, their practical utility is hindered by a significant challenge: the lack of interpretability. In this work, we introduce m
Externí odkaz:
https://doaj.org/article/a10ac788eea2460aaa7128cd70c4c3cc
Autor:
Jinkai Tian, Wenjing Yang
Publikováno v:
Entropy, Vol 26, Iss 11, p 902 (2024)
We introduce the concept-driven quantum neural network (CD-QNN), an innovative architecture designed to enhance the interpretability of quantum neural networks (QNNs). CD-QNN merges the representational capabilities of QNNs with the transparency of s
Externí odkaz:
https://doaj.org/article/7160de731bd043be9b596b1abad75477
Autor:
Sangeeta Yadav
Publikováno v:
AppliedMath, Vol 3, Iss 3, Pp 552-562 (2023)
We propose a Quantum Neural Network (QNN) for predicting stabilization parameter for solving Singularly Perturbed Partial Differential Equations (SPDE) using the Streamline Upwind Petrov Galerkin (SUPG) stabilization technique. SPDE-Q-Net, a QNN, is
Externí odkaz:
https://doaj.org/article/f5ecae55e8e446178ac0877821709975
Autor:
Marçal Comajoan Cara, Gopal Ramesh Dahale, Zhongtian Dong, Roy T. Forestano, Sergei Gleyzer, Daniel Justice, Kyoungchul Kong, Tom Magorsch, Konstantin T. Matchev, Katia Matcheva, Eyup B. Unlu
Publikováno v:
Axioms, Vol 13, Iss 5, p 323 (2024)
We introduce a hybrid quantum-classical vision transformer architecture, notable for its integration of variational quantum circuits within both the attention mechanism and the multi-layer perceptrons. The research addresses the critical challenge of
Externí odkaz:
https://doaj.org/article/ba7655e110f347c69ac7cffb2b367b8c
Publikováno v:
Mathematics, Vol 12, Iss 8, p 1230 (2024)
In recent years, advancements in brain science and neuroscience have significantly influenced the field of computer science, particularly in the domain of reinforcement learning (RL). Drawing insights from neurobiology and neuropsychology, researcher
Externí odkaz:
https://doaj.org/article/474a3b5a2019491e8102552d64cb05bd