Optimized continuous dynamical decoupling via differential geometry and machine learning

• URL: http://arxiv.org/abs/2310.08417v3
• Date: Fri, 18 Oct 2024 17:45:28 GMT
• Title: Optimized continuous dynamical decoupling via differential geometry and machine learning
• Authors: Nicolas André da Costa Morazotti, Adonai Hilário da Silva, Gabriel Audi, Felipe Fernandes Fanchini, Reginaldo de Jesus Napolitano,
• Abstract summary: We introduce a strategy to develop optimally designed fields for continuous dynamical decoupling. We obtain the optimal continuous field configuration to maximize the fidelity of a general one-qubit quantum gate.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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