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of 18
pro vyhledávání: '"Trenkwalder, Lea M."'
Hamiltonian simulation is believed to be one of the first tasks where quantum computers can yield a quantum advantage. One of the most popular methods of Hamiltonian simulation is Trotterization, which makes use of the approximation $e^{i\sum_jA_j}\s
Externí odkaz:
http://arxiv.org/abs/2311.04285
Autor:
Preti, Francesco, Schilling, Michael, Jerbi, Sofiene, Trenkwalder, Lea M., Nautrup, Hendrik Poulsen, Motzoi, Felix, Briegel, Hans J.
Publikováno v:
Quantum 8, 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:
http://arxiv.org/abs/2307.05744
Autor:
Trenkwalder, Lea M., Incera, Andrea López, Nautrup, Hendrik Poulsen, Flamini, Fulvio, Briegel, Hans J.
In recent years, reinforcement learning (RL) has become increasingly successful in its application to science and the process of scientific discovery in general. However, while RL algorithms learn to solve increasingly complex problems, interpreting
Externí odkaz:
http://arxiv.org/abs/2212.12743
Autor:
Ostaszewski, Mateusz, Trenkwalder, Lea M., Masarczyk, Wojciech, Scerri, Eleanor, Dunjko, Vedran
The study of Variational Quantum Eigensolvers (VQEs) has been in the spotlight in recent times as they may lead to real-world applications of near-term quantum devices. However, their performance depends on the structure of the used variational ansat
Externí odkaz:
http://arxiv.org/abs/2103.16089
Autor:
Nautrup, Hendrik Poulsen, Metger, Tony, Iten, Raban, Jerbi, Sofiene, Trenkwalder, Lea M., Wilming, Henrik, Briegel, Hans J., Renner, Renato
Publikováno v:
Mach. Learn.: Sci. Technol. 3, 045025, 2022
To make progress in science, we often build abstract representations of physical systems that meaningfully encode information about the systems. The representations learnt by most current machine learning techniques reflect statistical structure pres
Externí odkaz:
http://arxiv.org/abs/2001.00593
Autor:
Jerbi, Sofiene, Trenkwalder, Lea M., Nautrup, Hendrik Poulsen, Briegel, Hans J., Dunjko, Vedran
Publikováno v:
PRX Quantum 2, 010328 (2021)
In the past decade, the field of quantum machine learning has drawn significant attention due to the prospect of bringing genuine computational advantages to now widespread algorithmic methods. However, not all domains of machine learning have benefi
Externí odkaz:
http://arxiv.org/abs/1910.12760
Publikováno v:
Quantum Mach. Intell. 2, 13 (2020)
In recent years, the interest in leveraging quantum effects for enhancing machine learning tasks has significantly increased. Many algorithms speeding up supervised and unsupervised learning were established. The first framework in which ways to expl
Externí odkaz:
http://arxiv.org/abs/1910.11914
Autor:
Flamini, Fulvio, Hamann, Arne, Jerbi, Sofiène, Trenkwalder, Lea M., Nautrup, Hendrik Poulsen, Briegel, Hans J.
Publikováno v:
New J. Phys. 22 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:
http://arxiv.org/abs/1907.07503
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