Spectroscopy of Quantum Dot Orbitals with In-Plane Magnetic Fields

Camenzind, Leon C.; Yu, Liuqi; Stano, Peter; Zimmerman, Jeramy D.; Gossard, A. C.; Loss, Daniel; Zumbühl, Dominik M.

doi:10.1103/physrevlett.122.207701

Cited by 22 publications

(25 citation statements)

References 41 publications

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“…For the values assumed in panel (e), which correspond roughly to the interface parameters deduced from the experiment in Ref. 23, we would get the average g-factor of around −0.33. In that experiment, |g| ≈ 0.36 was fitted from spectral data.…”

Section: Correction Hierarchysupporting

confidence: 55%

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g -factor of electrons in gate-defined quantum dots in a strong in-plane magnetic field

et al. 2018

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We analyze orbital effects of an in-plane magnetic field on the spin structure of states of a gated quantum dot based in a two-dimensional electron gas. Starting with a k·p Hamiltonian, we perturbatively calculate these effects for the conduction band of GaAs, up to the third power of the magnetic field. We quantify several corrections to the g-tensor and reveal their relative importance. We find that for typical parameters, the Rashba spin-orbit term and the isotropic term, H43 ∝ P 2 B · σ, give the largest contributions in magnitude. The in-plane anisotropy of the g-factor is, on the other hand, dominated by the Dresselhaus spin-orbit term. At zero magnetic field, the total correction to the g-factor is typically 5-10% of its bulk value. In strong in-plane magnetic fields, the corrections are modified appreciably. arXiv:1808.03963v2 [cond-mat.mes-hall] 4 Dec 2018 3 We stick here to the triangular heterostructure confinement in Eq. (4) and do not discuss in the main text, for the sake of brevity, other confinement types considered in Ref. 22. We give results for a symmetric confinement in App. D. 4 The corrections resulting from such terms are expected to be much smaller than the terms denoted gz (see below), which are of similar origin and which are subdominant.

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Section: Correction Hierarchysupporting

confidence: 55%

“…The idea was conceived in Ref. 23, which demonstrated that the shape of quantum mechanical orbitals of a quantum dot can be inferred in this way. The information on the quantum dot shape thus acquired was essential for the experimental quantification of the spin-orbit couplings in Ref.…”

Section: Introductionmentioning

confidence: 99%

g -factor of electrons in gate-defined quantum dots in a strong in-plane magnetic field

et al. 2018

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“…Since the orientation of the magnetic field is usually tunable via a two-or threedimensional vector magnet, the study of anisotropic effects is well within the reach of state-of-the-art experiments. In situ control of the confinement is usually also available by allelectrical means via tuning the confinement gate voltages [85].…”

Section: Discussionmentioning

confidence: 99%

Exchange interaction of hole-spin qubits in double quantum dots in highly anisotropic semiconductors

Hetényi

Kloeffel

Loss

2020

Phys. Rev. Research

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We study the exchange interaction between two hole-spin qubits in a double quantum dot setup in a silicon nanowire in the presence of magnetic and electric fields. Based on symmetry arguments we show that there exists an effective spin that is conserved even in highly anisotropic semiconductors, provided that the system has a twofold symmetry with respect to the direction of the applied magnetic field. This finding facilitates the definition of qubit basis states and simplifies the form of exchange interaction for two-qubit gates in coupled quantum dots. If the magnetic field is applied along a generic direction, cubic anisotropy terms act as an effective spin-orbit interaction introducing novel exchange couplings even for an inversion symmetric setup. Considering the example of a silicon nanowire double dot, we present the relative strength of these anisotropic exchange interaction terms and calculate the fidelity of the √ SWAP gate. Furthermore, we show that the anisotropyinduced spin-orbit effects can be comparable to that of the direct Rashba spin-orbit interaction for experimentally feasible electric field strengths.

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“…A bias voltage V bias is applied to ohmic contacts to drive a current (I) through the device. The device schematic, designed for precise control of the confinement potential [15][16][17] , is shown in Fig. 1a.…”

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confidence: 99%

Machine learning enables completely automatic tuning of a quantum device faster than human experts

et al. 2020

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Variability is a problem for the scalability of semiconductor quantum devices. The parameter space is large, and the operating range is small. Our statistical tuning algorithm searches for specific electron transport features in gate-defined quantum dot devices with a gate voltage space of up to eight dimensions. Starting from the full range of each gate voltage, our machine learning algorithm can tune each device to optimal performance in a median time of under 70 minutes. This performance surpassed our best human benchmark (although both human and machine performance can be improved). The algorithm is approximately 180 times faster than an automated random search of the parameter space, and is suitable for different material systems and device architectures. Our results yield a quantitative measurement of device variability, from one device to another and after thermal cycling. Our machine learning algorithm can be extended to higher dimensions and other technologies.

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Spectroscopy of Quantum Dot Orbitals with In-Plane Magnetic Fields

Cited by 22 publications

References 41 publications

g -factor of electrons in gate-defined quantum dots in a strong in-plane magnetic field

g -factor of electrons in gate-defined quantum dots in a strong in-plane magnetic field

Exchange interaction of hole-spin qubits in double quantum dots in highly anisotropic semiconductors

Machine learning enables completely automatic tuning of a quantum device faster than human experts

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