Zobrazeno 1 - 10
of 34
pro vyhledávání: '"Glen Evenbly"'
Autor:
Glen Evenbly
Publikováno v:
Frontiers in Physics, Vol 10 (2022)
We propose a restricted class of tensor network state, built from number-state preserving tensors, for supervised learning tasks. This class of tensor network is argued to be a natural choice for classifiers as 1) they map classical data to classical
Externí odkaz:
https://doaj.org/article/2e35ec43ee08465fa6a82b52654dfa00
Autor:
Glen Evenbly
Publikováno v:
Frontiers in Applied Mathematics and Statistics, Vol 8 (2022)
We present an overview of the key ideas and skills necessary to begin implementing tensor network methods numerically, which is intended to facilitate the practical application of tensor network methods for researchers that are already versed with th
Externí odkaz:
https://doaj.org/article/75e83490ad2d48b79bf56003b3a29609
Publikováno v:
Quantum, Vol 5, p 546 (2021)
The study of low-dimensional quantum systems has proven to be a particularly fertile field for discovering novel types of quantum matter. When studied numerically, low-energy states of low-dimensional quantum systems are often approximated via a tens
Externí odkaz:
https://doaj.org/article/3e5c495068144c3eba2e65d950366b62
Autor:
Jutho Haegeman, Brian Swingle, Michael Walter, Jordan Cotler, Glen Evenbly, Volkher B. Scholz
Publikováno v:
Physical Review X, Vol 8, Iss 1, p 011003 (2018)
We construct entanglement renormalization schemes that provably approximate the ground states of noninteracting-fermion nearest-neighbor hopping Hamiltonians on the one-dimensional discrete line and the two-dimensional square lattice. These schemes g
Externí odkaz:
https://doaj.org/article/05b44a0ca5ca49bd92e17e98516fa75b
Autor:
Glen Evenbly
Understanding the collective behavior of a quantum many-body system, a system composed of a large number of interacting microscopic degrees of freedom, is a key aspect in many areas of contemporary physics. However, as a direct consequence of the dif
Externí odkaz:
http://espace.library.uq.edu.au/view/UQ:207551
Autor:
Glen Evenbly, Wangwei Lan
We propose a modified form of a tensor renormalization group algorithm for evaluating partition functions of classical statistical mechanical models on 2D lattices. This algorithm coarse-grains only the rows and columns of the lattice adjacent to a s
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::269b52deca78c44d53e44f09f694c221
http://arxiv.org/abs/1906.09283
http://arxiv.org/abs/1906.09283
Autor:
Steven R. White, Glen Evenbly
The representation of discrete, compact wavelet transformations (WTs) as circuits of local unitary gates is discussed. We employ a similar formalism as used in the multi-scale representation of quantum many-body wavefunctions using unitary circuits,
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::2daf158ba72dda20aa508a7d5d7050cb
https://resolver.caltech.edu/CaltechAUTHORS:20161031-090659507
https://resolver.caltech.edu/CaltechAUTHORS:20161031-090659507
Autor:
Michael Walter, Jutho Haegeman, Brian Swingle, Jordan Cotler, Glen Evenbly, Volkher B. Scholz
Publikováno v:
Physical Review X, Vol 8, Iss 1, p 011003 (2018)
Physical Review X
Physical Review X, 8(1):011003. American Physical Society
American Physical Society
PHYSICAL REVIEW X
Physical Review X, 8 (1)
Physical Review X
Physical Review X, 8(1):011003. American Physical Society
American Physical Society
PHYSICAL REVIEW X
Physical Review X, 8 (1)
We construct entanglement renormalization schemes which provably approximate the ground states of non-interacting fermion nearest-neighbor hopping Hamiltonians on the one-dimensional discrete line and the two-dimensional square lattice. These schemes
Publikováno v:
Quantum, Vol 5, p 546 (2021)
The study of low-dimensional quantum systems has proven to be a particularly fertile field for discovering novel types of quantum matter. When studied numerically, low-energy states of low-dimensional quantum systems are often approximated via a tens
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::dde48b69b46a86fa30d8d9b25cbde3fd
Autor:
Glen Evenbly, Andrew M. Goldsborough
Publikováno v:
Physical Review B
We propose a tensor network method for investigating strongly disordered systems that is based on an adaptation of entanglement renormalization [G. Vidal, Phys. Rev. Lett. 99, 220405 (2007)]. This method makes use of the strong disorder renormalizati