Zobrazeno 1 - 10
of 89
pro vyhledávání: '"Fanchini, Felipe"'
Autor:
Castelano, Leonardo K., Cunha, Iann, Luiz, Fabricio S., Prado, Marcelo V. de Souza, Fanchini, Felipe F.
Machine learning techniques are employed to perform the full characterization of a quantum system. The particular artificial intelligence technique used to learn the Hamiltonian is called physics informed neural network (PINN). The idea behind PINN i
Externí odkaz:
http://arxiv.org/abs/2310.15148
Autor:
Morazotti, Nicolas André da Costa, da Silva, Adonai Hilário, Audi, Gabriel, Fanchini, Felipe Fernandes, Napolitano, Reginaldo de Jesus
Publikováno v:
Phys. Rev. A 110, 042601 (2024)
We introduce a strategy to develop optimally designed fields for continuous dynamical decoupling. Using our methodology, we obtain the optimal continuous field configuration to maximize the fidelity of a general one-qubit quantum gate. To achieve thi
Externí odkaz:
http://arxiv.org/abs/2310.08417
Publikováno v:
Phys. Rev. A 109, 052411 (2024)
We have applied a machine learning algorithm to predict the emergence of environment-induced spontaneous synchronization between two qubits in an open system setting. In particular, we have considered three different models, encompassing global and l
Externí odkaz:
http://arxiv.org/abs/2308.15330
Autor:
da Silva, Adonai Hilário, Napolitano, Reginaldo de Jesus, Fanchini, Felipe Fernandes, Bellomo, Bruno
Publikováno v:
Phys. Rev. A 109, 032611 (2024)
We investigate the form required for the time-dependent Rabi frequencies involved in a procedure capable to protect the action of quantum gates on an atomic qutrit by means of external fields continuously decoupling the system from the environmental
Externí odkaz:
http://arxiv.org/abs/2212.07545
Publikováno v:
Phys. Rev. A 107, 022402 (2023)
Quantum machine learning is a growing research field that aims to perform machine learning tasks assisted by a quantum computer. Kernel-based quantum machine learning models are paradigmatic examples where the kernel involves quantum states, and the
Externí odkaz:
http://arxiv.org/abs/2209.11655
We apply the Krotov method for open and closed quantum systems with the objective of finding optimized controls to manipulate qubit/qutrit systems in the presence of the external environment. In the case of unitary optimization, the Krotov method is
Externí odkaz:
http://arxiv.org/abs/2208.03114
Publikováno v:
Quantum Machine Intelligence volume 5, 29 (2023)
Several applications of quantum machine learning (QML) rely on a quantum measurement followed by training algorithms using the measurement outcomes. However, recently developed QML models, such as variational quantum circuits (VQCs), can be implement
Externí odkaz:
http://arxiv.org/abs/2202.13964
Publikováno v:
Scientific Reports 13 (2023)
Machine learning has revolutionized many fields of science and technology. Through the $k$-Nearest Neighbors algorithm, we develop a model-independent classifier, where the algorithm can classify phases of a model to which it has never had access. Fo
Externí odkaz:
http://arxiv.org/abs/2109.00625
Autor:
Fanchini, Felipe Fernandes
Nessa dissertação, abordamos o problema de dois qubits interagindo com campos externos e entre si controladamente, de acordo com um Hamiltoniano considerado realista para implementação da porta lógica quântica XOR. Introduzimos acoplamentos ent
We consider probe-based quantum thermometry and show that machine classification can provide model-independent estimation with quantifiable error assessment. Our approach is based on the k-nearest-neighbor algorithm. The machine is trained using data
Externí odkaz:
http://arxiv.org/abs/2107.04555