Zobrazeno 1 - 10
of 302
pro vyhledávání: '"Gentile, Antonio"'
Autor:
Jaderberg, Ben, Gentile, Antonio A., Ghosh, Atiyo, Elfving, Vincent E., Jones, Caitlin, Vodola, Davide, Manobianco, John, Weiss, Horst
In this work we explore how quantum scientific machine learning can be used to tackle the challenge of weather modelling. Using parameterised quantum circuits as machine learning models, we consider two paradigms: supervised learning from weather dat
Externí odkaz:
http://arxiv.org/abs/2404.08737
Autor:
Williams, Chelsea A., Gentile, Antonio A., Elfving, Vincent E., Berger, Daniel, Kyriienko, Oleksandr
We propose quantum methods for solving differential equations that are based on a gradual improvement of the solution via an iterative process, and are targeted at applications in fluid dynamics. First, we implement the Jacobi iteration on a quantum
Externí odkaz:
http://arxiv.org/abs/2404.08605
Autor:
Jaderberg, Ben, Gentile, Antonio A., Berrada, Youssef Achari, Shishenina, Elvira, Elfving, Vincent E.
Parameterized quantum circuits as machine learning models are typically well described by their representation as a partial Fourier series of the input features, with frequencies uniquely determined by the feature map's generator Hamiltonians. Ordina
Externí odkaz:
http://arxiv.org/abs/2309.03279
Autor:
Ghosh, Atiyo, Gentile, Antonio A., Dagrada, Mario, Lee, Chul, Kim, Seong-Hyok, Cha, Hyukgeun, Choi, Yunjun, Kim, Brad, Kye, Jeong-Il, Elfving, Vincent E.
Publikováno v:
PMLR 202:11340-11359, 2023
Harmonic functions are abundant in nature, appearing in limiting cases of Maxwell's, Navier-Stokes equations, the heat and the wave equation. Consequently, there are many applications of harmonic functions from industrial process optimisation to robo
Externí odkaz:
http://arxiv.org/abs/2212.07462
Autor:
Gebhart, Valentin, Santagati, Raffaele, Gentile, Antonio Andrea, Gauger, Erik M., Craig, David, Ares, Natalia, Banchi, Leonardo, Marquardt, Florian, Pezze', Luca, Bonato, Cristian
Publikováno v:
Nat. Rev. Phys. (2023)
The future development of quantum technologies relies on creating and manipulating quantum systems of increasing complexity, with key applications in computation, simulation and sensing. This poses severe challenges in the efficient control, calibrat
Externí odkaz:
http://arxiv.org/abs/2207.00298
Accurate models of real quantum systems are important for investigating their behaviour, yet are difficult to distill empirically. Here, we report an algorithm -- the Quantum Model Learning Agent (QMLA) -- to reverse engineer Hamiltonian descriptions
Externí odkaz:
http://arxiv.org/abs/2112.08409
Autor:
Gentile, Antonio Francesco1 (AUTHOR) antoniofrancesco.gentile@icar.cnr.it, Macrì, Davide1 (AUTHOR) davide.macri@icar.cnr.it, Carnì, Domenico Luca2 (AUTHOR) dl.carni@dimes.unical.it, Greco, Emilio1 (AUTHOR) emilio.greco@icar.cnr.it, Lamonaca, Francesco2,3 (AUTHOR) f.lamonaca@dimes.unical.it
Publikováno v:
Sensors (14248220). May2024, Vol. 24 Issue 9, p2781. 22p.
Autor:
Gentile, Antonio, Fulgione, Andrea, Auzino, Barbara, Iovane, Valentina, Gallo, Daniela, Garramone, Raffaele, Iaccarino, Nunzia, Randazzo, Antonio, Iovane, Giuseppe, Cuomo, Paola, Capparelli, Rosanna, Iannelli, Domenico
Publikováno v:
In Infection, Genetics and Evolution March 2024 118
Autor:
Burridge, Ben M., Villarreal-Garcia, Gerardo E., Gentile, Antonio A., Jiang, Pisu, Barreto, Jorge
The continual success of superconducting photon-detection technologies in quantum photonics asserts cryogenic-compatible systems as a cornerstone of full quantum photonic integration. Here, we present a way to reversibly fine-tune the optical propert
Externí odkaz:
http://arxiv.org/abs/2105.04721