Zobrazeno 1 - 10
of 18
pro vyhledávání: '"Hendrik Poulsen Nautrup"'
Autor:
Francesco Preti, Michael Schilling, Sofiene Jerbi, Lea M. Trenkwalder, Hendrik Poulsen Nautrup, Felix Motzoi, Hans J. Briegel
Publikováno v:
Quantum, Vol 8, p 1343 (2024)
Shortening quantum circuits is crucial to reducing the destructive effect of environmental decoherence and enabling useful algorithms. Here, we demonstrate an improvement in such compilation tasks via a combination of using hybrid discrete-continuous
Externí odkaz:
https://doaj.org/article/272939a155be473089a4f6e37b0da189
Autor:
Sofiene Jerbi, Lukas J. Fiderer, Hendrik Poulsen Nautrup, Jonas M. Kübler, Hans J. Briegel, Vedran Dunjko
Publikováno v:
Nature Communications, Vol 14, Iss 1, Pp 1-8 (2023)
Comparing the capabilities of different quantum machine learning protocols is difficult. Here, the authors show that different learning models based on parametrized quantum circuits can all be seen as quantum linear models, thus driving general concl
Externí odkaz:
https://doaj.org/article/be9a37f1040943988bb2f3b9570a8b1a
Publikováno v:
Quantum, Vol 7, p 1206 (2023)
Learning a hidden property of a quantum system typically requires a series of interactions. In this work, we formalise such multi-round learning processes using a generalisation of classical-quantum states, called classical-quantum combs. Here, "clas
Externí odkaz:
https://doaj.org/article/bfa2c36ca8a94c268c4651b58cae2b76
Autor:
Lea M Trenkwalder, Andrea López-Incera, Hendrik Poulsen Nautrup, Fulvio Flamini, Hans J Briegel
Publikováno v:
Machine Learning: Science and Technology, Vol 4, Iss 3, p 035043 (2023)
In recent years, reinforcement learning (RL) has become increasingly successful in its application to the quantum domain and the process of scientific discovery in general. However, while RL algorithms learn to solve increasingly complex problems, in
Externí odkaz:
https://doaj.org/article/61bf0003a8ea4bb69c5f1e40ea217406
Publikováno v:
Nature Communications, Vol 8, Iss 1, Pp 1-8 (2017)
In the quest for fault-tolerant quantum computation, being able to interface different topological codes such as surface and color codes would allow to get the best of each code. Here, the authors show how to interface arbitrary topological quantum e
Externí odkaz:
https://doaj.org/article/905f911dc3cf4b0996bd28271f0911c1
Publikováno v:
PRX Quantum, Vol 2, Iss 1, p 010328 (2021)
Quantum algorithms have been successfully applied to provide computational speed ups to various machine-learning tasks and methods. A notable exception to this has been deep reinforcement learning (RL). Deep RL combines the power of deep neural netwo
Externí odkaz:
https://doaj.org/article/2837aa45135f47eeabbbfc10a14fc03d
Autor:
Fulvio Flamini, Arne Hamann, Sofiène Jerbi, Lea M Trenkwalder, Hendrik Poulsen Nautrup, Hans J Briegel
Publikováno v:
New Journal of Physics, Vol 22, Iss 4, p 045002 (2020)
The last decade has seen an unprecedented growth in artificial intelligence and photonic technologies, both of which drive the limits of modern-day computing devices. In line with these recent developments, this work brings together the state of the
Externí odkaz:
https://doaj.org/article/b10987c392ec4d82bef8f39c1ae9c88f
Publikováno v:
Quantum, Vol 3, p 215 (2019)
Quantum error correction is widely thought to be the key to fault-tolerant quantum computation. However, determining the most suited encoding for unknown error channels or specific laboratory setups is highly challenging. Here, we present a reinforce
Externí odkaz:
https://doaj.org/article/b9b16faded8c4cab9b6d63f3f344357c
Autor:
David T. Stephen, Hendrik Poulsen Nautrup, Juani Bermejo-Vega, Jens Eisert, Robert Raussendorf
Publikováno v:
Quantum, Vol 3, p 142 (2019)
Quantum phases of matter are resources for notions of quantum computation. In this work, we establish a new link between concepts of quantum information theory and condensed matter physics by presenting a unified understanding of symmetry-protected t
Externí odkaz:
https://doaj.org/article/18a08402321544ba8b2aef37e71a9d6e
Autor:
Sofiene Jerbi, Lukas J. Fiderer, Hendrik Poulsen Nautrup, Jonas M. Kübler, Hans J. Briegel, Vedran Dunjko
Machine learning algorithms based on parametrized quantum circuits are prime candidates for near-term applications on noisy quantum computers. In this direction, various types of quantum machine learning models have been introduced and studied extens
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::eea6504a2f075a3edceed627722b94a8
