Valley splittings in Si/SiGe quantum dots with a germanium spike in the silicon well

McJunkin, Thomas; MacQuarrie, E. R.; Tom, Leah; Neyens, Samuel F.; Dodson, J. P.; Thorgrimsson, Brandur; Corrigan, J.; Ercan, Hamdi; Savage, D. E.; Lagally, M. G.; Joynt, Robert; Coppersmith, S. N.; Friesen, Mark; Eriksson, M. A.

doi:10.1103/physrevb.104.085406

Cited by 30 publications

(26 citation statements)

References 34 publications

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“…To validate the synthetic noise and test the performance of the state classifiers, we generate a dataset of 756 manually labeled experimental images. This data was acquired using two quadruple QD devices, both fabricated on a Si/Si x Ge 1−x heterostructure in an accumulationmode overlapping aluminum gate architecture [28][29][30][31] and operated in a double dot configuration. The gatedefined QD devices use electric potentials defined by metallic gates to trap single electrons either in one central potential, or potentials on the left and right side of the device.…”

Section: B Experimental Datamentioning

confidence: 99%

Toward Robust Autotuning of Noisy Quantum dot Devices

et al. 2022

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The current autotuning approaches for quantum dot (QD) devices, while showing some success, lack an assessment of data reliability. This leads to unexpected failures when noisy or otherwise low quality data is processed by an autonomous system. In this work, we propose a framework for robust autotuning of QD devices that combines a machine learning (ML) state classifier with a data quality control module. The data quality control module acts as a "gatekeeper" system, ensuring that only reliable data is processed by the state classifier. Lower data quality results in either device recalibration or termination. To train both ML systems, we enhance the QD simulation by incorporating synthetic noise typical of QD experiments. We confirm that the inclusion of synthetic noise in the training of the state classifier significantly improves the performance, resulting in an accuracy of 95.0(9) % when tested on experimental data. We then validate the functionality of the data quality control module by showing the state classifier performance deteriorates with decreasing data quality, as expected. Our results establish a robust and flexible ML framework for autonomous tuning of noisy QD devices.

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Section: B Experimental Datamentioning

confidence: 99%

Toward Robust Autotuning of Noisy Quantum dot Devices

et al. 2022

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“…This large difference in behavior is demonstrated most obviously by the linear fits to the data, which we will compare below to numerical calculations of the valley splitting for many different atomistic realizations of the Wiggle Well. While tunable valley splittings (and closely related singlet-triplet splittings) of Si/SiGe quantum dots have recently been achieved by changing gate voltages 9,14,15,17,19 , the observed range of behavior has been modest: for example, 15% tunability with a maximum of E v = 213 μeV 15 or 140% tunability with a maximum of E v = 87 μeV 19 . Here in contrast, we report a striking > 440% tunability with a maximum of E v = 239 μeV.…”

Section: Resultsmentioning

confidence: 99%

“…Quantum dots formed in silicon-germanium heterostructures are promising candidates for quantum computing, but the degeneracy of the two conduction band minima (or "valleys") in silicon quantum wells can pose a challenge for forming qubits [1][2][3][4][5][6] . In such structures, the energy splitting between the valley states, E v , is typically tens to a few hundred μeV and can vary widely due to heterostructure design and unintentional defects [7][8][9][10][11][12][13][14][15][16][17][18][19] . The small size and intrinsic variability of E v has motivated several schemes for modifying or tuning its value.…”

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confidence: 99%

SiGe quantum wells with oscillating Ge concentrations for quantum dot qubits

et al. 2022

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Large-scale arrays of quantum-dot spin qubits in Si/SiGe quantum wells require large or tunable energy splittings of the valley states associated with degenerate conduction band minima. Existing proposals to deterministically enhance the valley splitting rely on sharp interfaces or modifications in the quantum well barriers that can be difficult to grow. Here, we propose and demonstrate a new heterostructure, the “Wiggle Well”, whose key feature is Ge concentration oscillations inside the quantum well. Experimentally, we show that placing Ge in the quantum well does not significantly impact our ability to form and manipulate single-electron quantum dots. We further observe large and widely tunable valley splittings, from 54 to 239 μeV. Tight-binding calculations, and the tunability of the valley splitting, indicate that these results can mainly be attributed to random concentration fluctuations that are amplified by the presence of Ge alloy in the heterostructure, as opposed to a deterministic enhancement due to the concentration oscillations. Quantitative predictions for several other heterostructures point to the Wiggle Well as a robust method for reliably enhancing the valley splitting in future qubit devices.

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“…One of them suggests back-gates for controlling the electric field across the QD independent from the QD filling [108]. Another approach uses engineering of the Ge profile across the Si/SiGe heterostructure [74,109]. A third method relies on increasing random fluctuations of alloy composition (Ge concentration in Si QW), which statistically increases average E VS,0 [71], but at the cost of larger variance.…”

Section: Discussionmentioning

confidence: 99%

Blueprint of a scalable spin qubit shuttle device for coherent mid-range qubit transfer in disordered Si/SiGe/SiO$_2$

Langrock¹,

Krzywda²,

Focke³

et al. 2022

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Silicon spin qubits stand out due to their very long coherence times, compatibility with industrial fabrication, and prospect to integrate classical control electronics. To achieve a truly scalable architecture, a coherent mid-range link between qubit registers has been suggested to solve the signal fan-out problem. Here we present a blueprint of such a ∼ 10 µm long link, called a spin qubit shuttle, which is based on connecting an array of gates into a small number of sets. The number of these gate sets and thus the number of required control signal is independent of the link distance to coherently shuttle the electron qubit. We discuss two different operation modes for the spin qubit shuttle: A qubit conveyor, i.e. a potential minimum that smoothly moves laterally, and a bucket brigade, in which the electron is transported through a series of tunnel-coupled quantum dots by adiabatic passage. We find the former approach more promising considering a realistic Si/SiGe device including potential disorder from the charged defects at the Si/SiO2 layer, as well as typical charge noise. Focusing on the qubit transfer fidelity of the conveyor shuttling mode, motional narrowing, the interplay between orbital and valley excitation and relaxation in presence of g-factors that depend on orbital and valley state of the electron, and effects from spin-hotspots are discussed in detail. We find that a transfer fidelity of 99.9 % is feasible in Si/SiGe at a speed of ∼10 m/s, if the average valley splitting and its inhomogeneity stay within realistic bounds. Operation at low global magnetic field ≈ 20 mT and material engineering towards high valley splitting is favourable to reach high transfer fidelities.

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Valley splittings in Si/SiGe quantum dots with a germanium spike in the silicon well

Cited by 30 publications

References 34 publications

Toward Robust Autotuning of Noisy Quantum dot Devices

Toward Robust Autotuning of Noisy Quantum dot Devices

SiGe quantum wells with oscillating Ge concentrations for quantum dot qubits

Blueprint of a scalable spin qubit shuttle device for coherent mid-range qubit transfer in disordered Si/SiGe/SiO$_2$

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