Zobrazeno 1 - 10
of 179
pro vyhledávání: '"Morten Hjorth-Jensen"'
Autor:
Jane Kim, Gabriel Pescia, Bryce Fore, Jannes Nys, Giuseppe Carleo, Stefano Gandolfi, Morten Hjorth-Jensen, Alessandro Lovato
Publikováno v:
Communications Physics, Vol 7, Iss 1, Pp 1-12 (2024)
Abstract Ultra-cold Fermi gases exhibit a rich array of quantum mechanical properties, including the transition from a fermionic superfluid Bardeen-Cooper-Schrieffer (BCS) state to a bosonic superfluid Bose-Einstein condensate (BEC). While these prop
Externí odkaz:
https://doaj.org/article/7c32d799950549a0a6bc27f18be95edc
Autor:
Niyaz R. Beysengulov, Øyvind S. Schøyen, Stian D. Bilek, Jonas B. Flaten, Oskar Leinonen, Morten Hjorth-Jensen, Johannes Pollanen, Håkon Emil Kristiansen, Zachary J. Stewart, Jared D. Weidman, Angela K. Wilson
Publikováno v:
PRX Quantum, Vol 5, Iss 3, p 030324 (2024)
The generation and evolution of entanglement in many-body systems is an active area of research that spans multiple fields, from quantum information science to the simulation of quantum many-body systems encountered in condensed matter, subatomic phy
Externí odkaz:
https://doaj.org/article/6b168435b4a74dde99d05af5dc393c2c
Autor:
Oliver Lerstøl Hebnes, Marianne Etzelmüller Bathen, Øyvind Sigmundson Schøyen, Sebastian G. Winther-Larsen, Lasse Vines, Morten Hjorth-Jensen
Publikováno v:
npj Computational Materials, Vol 8, Iss 1, Pp 1-15 (2022)
Abstract Semiconductor materials provide a compelling platform for quantum technologies (QT). However, identifying promising material hosts among the plethora of candidates is a major challenge. Therefore, we have developed a framework for the automa
Externí odkaz:
https://doaj.org/article/458c931e228a4469bcdab993a53843ca
Publikováno v:
Frontiers in Physics, Vol 11 (2023)
In this study, we explore the similarities and differences between variational Monte Carlo techniques that employ conventional and artificial neural network representations of the ground-state wave function for fermionic systems. Our primary focus is
Externí odkaz:
https://doaj.org/article/f31f0584f0c0409f82f3f273d3c064f3
Autor:
Bryce Fore, Jane M. Kim, Giuseppe Carleo, Morten Hjorth-Jensen, Alessandro Lovato, Maria Piarulli
Publikováno v:
Physical Review Research, Vol 5, Iss 3, p 033062 (2023)
Low-density neutron matter is characterized by fascinating emergent quantum phenomena, such as the formation of Cooper pairs and the onset of superfluidity. We model this density regime by capitalizing on the expressivity of the hidden-nucleon neural
Externí odkaz:
https://doaj.org/article/82a2c3f17f9c4b13bc634c2417c58f6f
Publikováno v:
PLoS ONE, Vol 15, Iss 11, p e0242334 (2020)
The time it takes a student to graduate with a university degree is mitigated by a variety of factors such as their background, the academic performance at university, and their integration into the social communities of the university they attend. D
Externí odkaz:
https://doaj.org/article/eb15be37c09c43bd9a3b0f95a007a2df
Publikováno v:
Uniped, Vol 38, Iss 4, Pp 303-310 (2015)
Beregninger har blitt et grunnleggende verktøy i utøvelsen av fysikkfaget både i forskning og industri, men det har tradisjonelt ikke vært en integrert del av undervisningens innhold eller form i fysikk. Vi har integrert beregninger i utdanningen
Externí odkaz:
https://doaj.org/article/6b4bdab8c1534ebdb69edde8b591103f
We present a variational Monte Carlo method that solves the nuclear many-body problem in the occupation number formalism exploiting an artificial neural network representation of the ground-state wave function. A memory-efficient version of the stoch
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::fbf2b7bb5e7bc3fc39d5f442bab22dec
http://arxiv.org/abs/2211.04614
http://arxiv.org/abs/2211.04614
Autor:
Amber Boehnlein, Markus Diefenthaler, Nobuo Sato, Malachi Schram, Veronique Ziegler, Cristiano Fanelli, Morten Hjorth-Jensen, Tanja Horn, Michelle P. Kuchera, Dean Lee, Witold Nazarewicz, Peter Ostroumov, Kostas Orginos, Alan Poon, Xin-Nian Wang, Alexander Scheinker, Michael S. Smith, Long-Gang Pang
Publikováno v:
Reviews of Modern Physics. 94
Advances in machine learning methods provide tools that have broad applicability in scientific research. These techniques are being applied across the diversity of nuclear physics research topics, leading to advances that will facilitate scientific d
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::22f5753d02abba643b67daff8a41a2d8
https://doi.org/10.1103/revmodphys.94.031003
https://doi.org/10.1103/revmodphys.94.031003
Autor:
Jack Henderson, M. Spieker, D. Weisshaar, Brenden Longfellow, Brandon Elman, P. C. Bender, J. Ash, B. A. Brown, R. Elder, M. Grinder, Morten Hjorth-Jensen, Ching-Yen Wu, Alexandra Gade, H. Iwasaki, D. Rhodes, T. Mijatović
Publikováno v:
Physical Review C. 103
The shape and collectivity of $^{106}\mathrm{Cd}$ was investigated via a sub-barrier-energy Coulomb excitation experiment performed at the National Superconducting Cyclotron Laboratory Re-accelerator facility using the JANUS setup. Transition matrix
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::8ed9494130834ea0e64994875d3f1751
https://doi.org/10.1103/physrevc.103.l051301
https://doi.org/10.1103/physrevc.103.l051301