Zobrazeno 1 - 10
of 584
pro vyhledávání: '"tensor networks"'
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-16 (2024)
Abstract Tensor networks are emerging architectures for implementing quantum classification models. The branching multi-scale entanglement renormalization ansatz (BMERA) is a tensor network known for its enhanced entanglement properties. This paper i
Externí odkaz:
https://doaj.org/article/80c5af95134c4b34ab9e1aadad40321b
Autor:
Nina Glaser, Markus Reiher
Publikováno v:
CHIMIA, Vol 78, Iss 4 (2024)
Many complex chemical problems encoded in terms of physics-based models become computationallyintractable for traditional numerical approaches due to their unfavorable scaling with increasing molecular size. Tensor decomposition techniques can overco
Externí odkaz:
https://doaj.org/article/c0c43ccdc5eb4a7db337f7386e75f9f1
Akademický článek
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Autor:
Simon, Steven H., author
Publikováno v:
Topological Quantum, 2023.
Externí odkaz:
https://doi.org/10.1093/oso/9780198886723.003.0038
Autor:
Farnaz Sedighin
Publikováno v:
Journal of Medical Signals and Sensors, Vol 14, Iss 6, Pp 16-16 (2024)
In the past decade, tensors have become increasingly attractive in different aspects of signal and image processing areas. The main reason is the inefficiency of matrices in representing and analyzing multimodal and multidimensional datasets. Matrice
Externí odkaz:
https://doaj.org/article/c02e8589aa654bfea8a009ffa7772c52
Publikováno v:
Machine Learning: Science and Technology, Vol 5, Iss 4, p 045012 (2024)
Tensor networks (TNs) have seen an increase in applications in recent years. While they were originally developed to model many-body quantum systems, their usage has expanded into the field of machine learning. This work adds to the growing range of
Externí odkaz:
https://doaj.org/article/e218d5bc7ace40578a852451fdf95f8c
Publikováno v:
Machine Learning: Science and Technology, Vol 5, Iss 2, p 025064 (2024)
Recent years have witnessed an increased interest in recovering dynamical laws of complex systems in a largely data-driven fashion under meaningful hypotheses. In this work, we propose a scalable and numerically robust method for this task, utilizing
Externí odkaz:
https://doaj.org/article/4c97c34240744c98947d3b62b66e87e6
Publikováno v:
Machine Learning: Science and Technology, Vol 5, Iss 1, p 015048 (2024)
There have been numerous quantum neural networks reported, but they struggle to match traditional neural networks in accuracy. Given the huge improvement of the neural network models’ accuracy by two-dimensional tensor network (TN) states in classi
Externí odkaz:
https://doaj.org/article/f999f6fd687b443095ca2b84225931de
Publikováno v:
New Journal of Physics, Vol 26, Iss 2, p 023024 (2024)
Tensor network codes enable structured construction and manipulation of stabilizer codes out of small seed codes. Here, we apply reinforcement learning (RL) to tensor network code geometries and demonstrate how optimal stabilizer codes can be found.
Externí odkaz:
https://doaj.org/article/3b02e8090624479380cda0befa609512
Akademický článek
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