Quantum mechanical modeling of electron spins in realistic gate defined double quantum dots.
ORAL
Abstract
Spin qubits in semiconductors are front candidates for quantum computation, given their long coherence times [1]. Benefiting from decades of development in CMOS technologies [2], electrons in gate-defined quantum dots promise a platform for large scale integration of spin qubits. With recent demonstrations of high fidelity two-qubit gates[3,4], comes the need for active control over the exchange interaction that drives them. As such, a more comprehensive understanding of the exchange interaction in realistic devices and its dependence on control and environmental parameters is critical for spin-based quantum computation. The conditions for qubits' operation, on a small or large scale, are dictated by the underlying device architecture, choice of material, externally applied voltages and charge noise environment which has been shown to be a limiting factor for spin qubits. In this work, we propose a modelling framework that bridges a devices physical and operational conditions to the qubit energy space. The model combines electrostatic simulations and full configuration interaction (FCI) methods to estimate exchange interaction. Furthermore, the inclusion of any external potentials allows to study the effect of charge noise. This model opens a window for a deeper insight into spin qubit operation in semiconductors by estimating a critical metric represented in exchange coupling, its dependence on a device’s physical parameters and its sensitivity to the performance-limiting charge noise.
[1] Veldhorst, M. et al. Nat. Nanotechnology (2014)
[2] Li, R et al, IEDM (2020)
[3] Noiri,A. et al, Nature (2022)
[4] Xue, X. et al, Natue (2022)
[1] Veldhorst, M. et al. Nat. Nanotechnology (2014)
[2] Li, R et al, IEDM (2020)
[3] Noiri,A. et al, Nature (2022)
[4] Xue, X. et al, Natue (2022)
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Publication: M.M El Kordy Shehata et al, Modelling semiconductor spin qubits and their charge noise environment for quantum gate fidelity estimation, arXiv.2210.04539(2022).
Presenters
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M.Mohamed El Kordy Shehata
IMEC / KU Leuven
Authors
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M.Mohamed El Kordy Shehata
IMEC / KU Leuven
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George Simion
IMEC
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Fahd A. Mohiyaddin
IMEC
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Ruoyu Li
imec, IMEC
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Asser Elsayed
IMEC / KU Leuven
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Clement Godfrin
imec, IMEC
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Danny Wan
IMEC, imec
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Massimo Mongillo
IMEC, imec
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Kristiaan De Greve
IMEC, imec, IMEC / KU Leuven
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Pol Van Dorpe
imec, IMEC / KU Leuven