Zobrazeno 1 - 10
of 43
pro vyhledávání: '"Nautrup, Hendrik Poulsen"'
Any quantum computation consists of a sequence of unitary evolutions described by a finite set of Hamiltonians. When this set is taken to consist of only products of Pauli operators, we show that the minimal such set generating $\mathfrak{su}(2^{N})$
Externí odkaz:
http://arxiv.org/abs/2408.03294
Measurement-based quantum computation (MBQC) is a paradigm for quantum computation where computation is driven by local measurements on a suitably entangled resource state. In this work we show that MBQC is related to a model of quantum computation b
Externí odkaz:
http://arxiv.org/abs/2312.13185
Autor:
Majumder, Arunava, Krumm, Marius, Radkohl, Tina, Nautrup, Hendrik Poulsen, Jerbi, Sofiene, Briegel, Hans J.
Measurement-based quantum computation (MBQC) offers a fundamentally unique paradigm to design quantum algorithms. Indeed, due to the inherent randomness of quantum measurements, the natural operations in MBQC are not deterministic and unitary, but ar
Externí odkaz:
http://arxiv.org/abs/2310.13524
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
Symmetry protected topological phases exhibit nontrivial short-ranged entanglement protected by symmetry and cannot be adiabatically connected to trivial product states while preserving the symmetry. In contrast, intrinsic topological phases do not n
Externí odkaz:
http://arxiv.org/abs/2305.09747
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:
Smith, Isaac D., Krumm, Marius, Fiderer, Lukas J., Nautrup, Hendrik Poulsen, Briegel, Hans J.
Publikováno v:
Quantum 7, 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:
http://arxiv.org/abs/2212.00553
Autor:
Jerbi, Sofiene, Fiderer, Lukas J., Nautrup, Hendrik Poulsen, Kübler, Jonas M., Briegel, Hans J., Dunjko, Vedran
Publikováno v:
Nature Communications 14, 517 (2023)
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:
http://arxiv.org/abs/2110.13162
Autor:
Erhard, Alexander, Nautrup, Hendrik Poulsen, Meth, Michael, Postler, Lukas, Stricker, Roman, Ringbauer, Martin, Schindler, Philipp, Briegel, Hans J., Blatt, Rainer, Friis, Nicolai, Monz, Thomas
Publikováno v:
Nature 589, 220-224 (2021)
Future quantum computers will require quantum error correction for faithful operation. The correction capabilities come with an overhead for performing fault-tolerant logical operations on the encoded qubits. One of the most resource efficient ways t
Externí odkaz:
http://arxiv.org/abs/2006.03071
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