Optimized continuous dynamical decoupling via differential geometry and machine learning
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2023
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| _version_ | 1866916443542519808 |
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| author | Morazotti, Nicolas André da Costa da Silva, Adonai Hilário Audi, Gabriel Fanchini, Felipe Fernandes Napolitano, Reginaldo de Jesus |
| author_facet | Morazotti, Nicolas André da Costa da Silva, Adonai Hilário Audi, Gabriel Fanchini, Felipe Fernandes Napolitano, Reginaldo de Jesus |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2310_08417 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Optimized continuous dynamical decoupling via differential geometry and machine learning Morazotti, Nicolas André da Costa da Silva, Adonai Hilário Audi, Gabriel Fanchini, Felipe Fernandes Napolitano, Reginaldo de Jesus Quantum Physics 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. |
| title | Optimized continuous dynamical decoupling via differential geometry and machine learning |
| topic | Quantum Physics |
| url | https://arxiv.org/abs/2310.08417 |