Machine learning and its applications in quantum physics: metrology, thermology, p...
Fundamentals of the Qualitative Theory of Differential Equations
Development of semi-supervised learning techniques via collective dynamical systems
| Full text | |
| Author(s): |
da Costa Morazotti, Nicolas Andre
;
da Silva, Adonai Hilario
;
Audi, Gabriel
;
Fanchini, Felipe Fernandes
;
de Jesus Napolitano, Reginaldo
Total Authors: 5
|
| Document type: | Journal article |
| Source: | PHYSICAL REVIEW A; v. 110, n. 4, p. 14-pg., 2024-10-01. |
| Abstract | |
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 this, considering dephasing-noise perturbations, we employ an auxiliary qubit instead of the boson bath to implement a purification scheme, which results in unitary dynamics. Employing the sub-Riemannian geometry framework for the two-qubit unitary group, we derive and numerically solve the geodesic equations, obtaining the optimal time-dependent control Hamiltonian. Also, due to the extended time required to find solutions to the geodesic equations, we train a neural network on a subset of geodesic solutions, enabling us to promptly generate the time-dependent control Hamiltonian for any desired gate, which is crucial in circuit optimization. (AU) | |
| FAPESP's process: | 18/00796-3 - Quantum-information processing under the auspices of the scattering-matrix formalism |
| Grantee: | Reginaldo de Jesus Napolitano |
| Support Opportunities: | Regular Research Grants |
| FAPESP's process: | 23/04987-6 - Quantum optimization and machine learning: variational algorithms and applications |
| Grantee: | Felipe Fernandes Fanchini |
| Support Opportunities: | Regular Research Grants |