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Spin Polarization through Floquet Resonances in a Driven Central Spin Model.
Pieter W Claeys1,2,3, Stijn De Baerdemacker2, Omar El Araby1
1Institute for Theoretical Physics Amsterdam and Delta Institute for Theoretical Physics, University of Amsterdam, Science Park 904, 1098 XH Amsterdam, Netherlands.
Physical Review Letters
|September 8, 2018
Summary
Controlled driving of quantum systems enables transitions between eigenstates. This method, applied to the central spin model, achieves resonance-based decoupling from environmental decoherence.
Area of Science:
- Quantum physics
- Many-body systems
- Quantum control
Background:
- Periodically driven quantum systems exhibit complex behaviors.
- Controlling quantum many-body systems is crucial for quantum technologies.
- Floquet engineering offers pathways for manipulating quantum states.
Purpose of the Study:
- To demonstrate controlled transitions between eigenstates in driven quantum systems.
- To apply adiabatic frequency sweeps for quantum many-body control.
- To achieve polarization-based decoupling of a central spin from its environment.
Main Methods:
- Adiabatic frequency variation of periodically driven systems.
- Utilizing the central spin model as a test case.
- Employing quantum quench techniques to construct the Floquet Hamiltonian.
- Restricting the many-body basis for Hamiltonian construction.
Main Results:
- Controlled transitions between resonant eigenstates are induced by adiabatic frequency sweeps.
- A polarization-based decoupling mechanism for the central spin is achieved at resonance.
- The Floquet Hamiltonian is explicitly constructed in a restricted basis for the central spin model.
- Floquet resonances are effectively modeled using quantum quench techniques.
Conclusions:
- Adiabatic frequency sweeps provide a general tool for quantum many-body control.
- Resonance conditions enable effective decoupling of a central spin from environmental decoherence.
- Integrability of the central spin model allows for explicit Floquet Hamiltonian construction and modeling.
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