Zobrazeno 1 - 10
of 31
pro vyhledávání: '"Meyer, Johannes Jakob"'
Characterizing quantum systems is a fundamental task that enables the development of quantum technologies. Various approaches, ranging from full tomography to instances of classical shadows, have been proposed to this end. However, quantum states tha
Externí odkaz:
http://arxiv.org/abs/2407.12916
Quantum machine learning aims to improve learning methods through the use of quantum computers. If it is to ever realize its potential, many obstacles need to be overcome. A particularly pressing one arises at the prediction stage because the outputs
Externí odkaz:
http://arxiv.org/abs/2406.13812
Autor:
Nielsen, Jens A. H., Kicinski, Mateusz, Arge, Tummas N., Vijayadharan, Kannan, Foldager, Jonathan, Borregaard, Johannes, Meyer, Johannes Jakob, Neergaard-Nielsen, Jonas S., Gehring, Tobias, Andersen, Ulrik L.
Variational quantum algorithms (VQAs) are hybrid quantum-classical approaches used for tackling a wide range of problems on noisy intermediate-scale quantum (NISQ) devices. Testing these algorithms on relevant hardware is crucial to investigate the e
Externí odkaz:
http://arxiv.org/abs/2312.13870
Autor:
Sweke, Ryan, Recio, Erik, Jerbi, Sofiene, Gil-Fuster, Elies, Fuller, Bryce, Eisert, Jens, Meyer, Johannes Jakob
Quantum machine learning is arguably one of the most explored applications of near-term quantum devices. Much focus has been put on notions of variational quantum machine learning where parameterized quantum circuits (PQCs) are used as learning model
Externí odkaz:
http://arxiv.org/abs/2309.11647
In quantum metrology, one of the major applications of quantum technologies, the ultimate precision of estimating an unknown parameter is often stated in terms of the Cram\'er-Rao bound. Yet, the latter is no longer guaranteed to carry an operational
Externí odkaz:
http://arxiv.org/abs/2307.06370
Publikováno v:
Nature Phys. 20, 1648 (2024)
Quantum error mitigation has been proposed as a means to combat unwanted and unavoidable errors in near-term quantum computing without the heavy resource overheads required by fault tolerant schemes. Recently, error mitigation has been successfully a
Externí odkaz:
http://arxiv.org/abs/2210.11505
Publikováno v:
Phys. Rev. Lett. 131, 100803 (2023)
The advent of noisy intermediate-scale quantum computers has put the search for possible applications to the forefront of quantum information science. One area where hopes for an advantage through near-term quantum computers are high is quantum machi
Externí odkaz:
http://arxiv.org/abs/2206.11740
Autor:
Meyer, Johannes Jakob, Mularski, Marian, Gil-Fuster, Elies, Mele, Antonio Anna, Arzani, Francesco, Wilms, Alissa, Eisert, Jens
Publikováno v:
PRX Quantum 4, 010328 (2023)
Variational quantum machine learning is an extensively studied application of near-term quantum computers. The success of variational quantum learning models crucially depends on finding a suitable parametrization of the model that encodes an inducti
Externí odkaz:
http://arxiv.org/abs/2205.06217
Publikováno v:
Quantum 5, 582 (2021)
A large body of recent work has begun to explore the potential of parametrized quantum circuits (PQCs) as machine learning models, within the framework of hybrid quantum-classical optimization. In particular, theoretical guarantees on the out-of-samp
Externí odkaz:
http://arxiv.org/abs/2106.03880
Autor:
Hubregtsen, Thomas, Wierichs, David, Gil-Fuster, Elies, Derks, Peter-Jan H. S., Faehrmann, Paul K., Meyer, Johannes Jakob
Publikováno v:
Phys. Rev. A 106, 042431 (2022)
Kernel methods are a cornerstone of classical machine learning. The idea of using quantum computers to compute kernels has recently attracted attention. Quantum embedding kernels (QEKs) constructed by embedding data into the Hilbert space of a quantu
Externí odkaz:
http://arxiv.org/abs/2105.02276