Zobrazeno 1 - 10
of 252
pro vyhledávání: '"Enrico Prati"'
Publikováno v:
Quantum Reports, Vol 6, Iss 1, Pp 1-13 (2023)
The Quantum Amplitude Estimation (QAE) algorithm is a major quantum algorithm designed to achieve a quadratic speed-up. Until fault-tolerant quantum computing is achieved, being competitive over classical Monte Carlo (MC) remains elusive. Alternative
Externí odkaz:
https://doaj.org/article/eb20aee64e1f42edafaa6e11b737006f
A Tutorial on the Use of Physics-Informed Neural Networks to Compute the Spectrum of Quantum Systems
Publikováno v:
Technologies, Vol 12, Iss 10, p 174 (2024)
Quantum many-body systems are of great interest for many research areas, including physics, biology, and chemistry. However, their simulation is extremely challenging, due to the exponential growth of the Hilbert space with system size, making it exc
Externí odkaz:
https://doaj.org/article/2bc8c30f2cdf4322a7fc3d6e36b17f2b
Autor:
Lorenzo Moro, Enrico Prati
Publikováno v:
Communications Physics, Vol 6, Iss 1, Pp 1-10 (2023)
Abstract Quantum machine learning promises to revolutionize traditional machine learning by efficiently addressing hard tasks for classical computation. While claims of quantum speed-up have been announced for gate-based quantum computers and photon-
Externí odkaz:
https://doaj.org/article/5f83d1ee3b0a4150904296763644157d
Publikováno v:
New Journal of Physics, Vol 26, Iss 10, p 103015 (2024)
The use of deep learning in physical sciences has recently boosted the ability of researchers to tackle physical systems where little or no analytical insight is available. Recently, the Physics−Informed Neural Networks (PINNs) have been introduced
Externí odkaz:
https://doaj.org/article/ca3bcd7182704e64b28233ea7e427c58
Autor:
Gabriele Agliardi, Enrico Prati
Publikováno v:
Quantum Reports, Vol 4, Iss 1, Pp 75-105 (2022)
Loading data efficiently from classical memories to quantum computers is a key challenge of noisy intermediate-scale quantum computers. Such a problem can be addressed through quantum generative adversarial networks (qGANs), which are noise tolerant
Externí odkaz:
https://doaj.org/article/7ed4b2d37f24489e82804c2f8648014c
Publikováno v:
Physics Letters B, Vol 832, Iss , Pp 137228- (2022)
We apply quantum integration to elementary particle-physics processes. In particular, we look at scattering processes such as e+e−→qq¯ and e+e−→qq¯′W. The corresponding probability distributions can be first appropriately loaded on a quan
Externí odkaz:
https://doaj.org/article/e9f5cc87061d4ff2bfd3e796480af02c
Publikováno v:
Communications Physics, Vol 4, Iss 1, Pp 1-8 (2021)
Quantum compilers are characterized by a trade-off between the length of the sequences, the precompilation time, and the execution time. Here, the authors propose an approach based on deep reinforcement learning to approximate unitary operators as ci
Externí odkaz:
https://doaj.org/article/ab73e5736a1642b98d3c091262945c6e
Autor:
Giulio Tavani, Chiara Barri, Erfan Mafakheri, Giorgia Franzò, Michele Celebrano, Michele Castriotta, Matteo Di Giancamillo, Giorgio Ferrari, Francesco Picciariello, Giulio Foletto, Costantino Agnesi, Giuseppe Vallone, Paolo Villoresi, Vito Sorianello, Davide Rotta, Marco Finazzi, Monica Bollani, Enrico Prati
Publikováno v:
Materials, Vol 16, Iss 6, p 2344 (2023)
Recent advancements in quantum key distribution (QKD) protocols opened the chance to exploit nonlaser sources for their implementation. A possible solution might consist in erbium-doped light emitting diodes (LEDs), which are able to produce photons
Externí odkaz:
https://doaj.org/article/3af94e4a6806424684c633ac974622fa
Publikováno v:
npj Quantum Information, Vol 3, Iss 1, Pp 1-14 (2017)
Abstract Even the quantum simulation of an apparently simple molecule such as Fe2S2 requires a considerable number of qubits of the order of 106, while more complex molecules such as alanine (C3H7NO2) require about a hundred times more. In order to a
Externí odkaz:
https://doaj.org/article/a5d67fed2ed349b7acaa148e6c77fa98
Autor:
Yuma Takahashi, Tomoki Ishii, Kaisei Uchida, Takumi Zushi, Lindsay Coe, Shin-ichiro Sato, Enrico Prati, Takahiro Shinada, Takashi Tanii
Publikováno v:
e-Journal of Surface Science and Nanotechnology.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::524f0611de18cf9c66349c85bd027f94
https://doi.org/10.1380/ejssnt.2023-041
https://doi.org/10.1380/ejssnt.2023-041