Monolithic Integration of a Metalens with a Surface Trap for Efficient Readout of Trapped Ion Qubits over a Wide Area
ORAL
Abstract
To achieve utility-scale quantum computing, the size of current quantum systems must be increased by orders of magnitude while maintaining high readout fidelities. For trapped-ion quantum computers, this implies a need for a system to read out trapped ion qubits over a wide area while maintaining a collection efficiency. With a collection optic such as a compound microscope, collection efficiency decreases with the distance between the trapped ions and the optical axis of the optic. We define the effective width of object plane (EWOP) as the width of the object plane of trapped ions over which ion fluorescence can be collected with efficiencies at least 80 % of the maximum. In general, EWOP decreases as the numerical aperture of the collimating lens increases. Here, we miniaturize the collimating lens of our compound microscope and integrate it directly with the surface trap. This can be repeated for each trap across a full-scale system to achieve a higher EWOP while maintaining a collection efficiency. The EWOP of such an optical system is roughly limited only by two factors: the total width over which collimating lenses are integrated, and the width of the focusing lens. Previous attempts to integrate lenses with surface traps have relied on manual alignment. In our work, we monolithically integrate a collimating metalens with a surface trap. We compare its performance to that of the same trap but with an unintegrated bulk collimating lens. With the same focusing lens, the maximum EWOP of the system with/without the integration was measured to be 5.00/1.09 mm while the collection efficiency was measured to be 0.68/1.06 %.
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Presenters
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Hae M Lim
University of Washington at Seattle, University of Washington
Authors
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Hae M Lim
University of Washington at Seattle, University of Washington
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Johannes Froech
University of Washington at Seattle
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Christian Pluchar
University of Washington at Seattle
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Arka Majumdar
University of Washington at Seattle
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Sara Mouradian
University of Washington