arXiv subjects

Lucas Stehouwer

Publications and source records attributed to Lucas Stehouwer.

8 recordsLinked to original sources

arXiv2026

A Degenerate Singlet-Triplet Qubit with All-Electrical Orthogonal Control

Singlet-triplet qubits offer an attractive encoding for semiconductor quantum computing, combining ancilla-free readout, reduced sensitivity to common-mode noise, and baseband voltage control. However, the Zeeman energy difference $\Delta E_\mathrm{Z}$ is typically fixed by local magnetic field gradients or $g$-factor inhomogeneities, leaving the exchange interaction $J$ as the only dynamically tunable parameter. This always-on $\Delta E_\mathrm{Z}$ precludes orthogonal control of the qubit's rotation axes and introduces unwanted state rotations during idling. Here we demonstrate all-electrical orthogonal control of a degenerate singlet-triplet (DST) qubit formed by two hole spins in a germanium double quantum dot. Exploiting the electrically tunable anisotropic $g$-factors of the two spins, we identify a regime where both $\Delta E_\mathrm{Z}$ and $J$ vanish, making the $S$ and $T_0$ states degenerate at the idle point. By applying only baseband voltage pulses, we independently control both $J$ and $\Delta E_\mathrm{Z}$, enabling fully orthogonal $Z$- and $X$-axis rotations. Randomized benchmarking yields an average physical single-qubit gate fidelity of 99.53\% for a gate duration of approximately 100 ns. Finally, we electrically tune the degenerate point across a wide range of magnetic field orientations, enabling operation in a regime of enhanced coherence time and offering a route towards multi-qubit scaling under a shared global magnetic field.

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arXiv2025

Towards autonomous time-calibration of large quantum-dot devices: Detection, real-time feedback, and noise spectroscopy

The performance and scalability of semiconductor quantum-dot (QD) qubits are limited by electrostatic drift and charge noise that shift operating points and destabilize qubit parameters. As systems expand to large one- and two-dimensional arrays, manual recalibration becomes impractical, creating a need for autonomous stabilization frameworks. Here, we introduce a method that uses the full network of charge-transition lines in repeatedly acquired double-quantum-dot charge stability diagrams (CSDs) as a multidimensional probe of the local electrostatic environment. By accurately tracking the motion of selected transitions in time, we detect voltage drifts, identify abrupt charge reconfigurations, and apply compensating updates to maintain stable operating conditions. We demonstrate our approach on a 10-QD device, showing robust stabilization and real-time diagnostic access to dot-specific noise processes. The high acquisition rate of radio-frequency reflectometry CSD measurements also enables time-domain noise spectroscopy, allowing the extraction of noise power spectral densities, the identification of two-level fluctuators, and the analysis of spatial noise correlations across the array. From our analysis, we find that the background noise at 100~$\mu$\si{\hertz} is dominated by drift with a power law of $1/f^2$, accompanied by a few dominant two-level fluctuators and an average linear correlation length of $(188 \pm 38)$~\si{\nano\meter} in the device. These capabilities form the basis of a scalable, autonomous calibration and characterization module for QD-based quantum processors, providing essential feedback for long-duration, high-fidelity qubit operations.

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arXiv2025

Automated electrostatic characterization of quantum dot devices in single- and bilayer heterostructures

As quantum dot (QD)-based spin qubits advance toward larger, more complex device architectures, rapid, automated device characterization and data analysis tools become critical. The orientation and spacing of transition lines in a charge stability diagram (CSD) contain a fingerprint of a QD device's capacitive environment, making these measurements useful tools for device characterization. However, manually interpreting these features is time-consuming, error-prone, and impractical at scale. Here, we present an automated protocol for extracting underlying capacitive properties from CSDs. Our method integrates machine learning, image processing, and object detection to identify and track charge transitions across large datasets without manual labeling. We demonstrate this method using experimentally measured data from a strained-germanium single-quantum-well (planar) and a strained-germanium double-quantum-well (bilayer) QD device. Unlike for planar QD devices, CSDs in bilayer germanium heterostructure exhibit a larger set of transitions, including interlayer tunneling and distinct loading lines for the vertically stacked QDs, making them a powerful testbed for automation methods. By analyzing the properties of many CSDs, we can statistically estimate physically relevant quantities, like relative lever arms and capacitive couplings. Thus, our protocol enables rapid extraction of useful, nontrivial information about QD devices.

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arXiv2025

A Three-Dimensional Array of Quantum Dots

Quantum dots can confine single electrons or holes to define spin qubits that can be operated with high fidelity. Experimental work has progressed from linear to two-dimensional arrays of quantum dots, enabling qubit interactions that are essential for quantum simulation and computation. Here, we explore architectures beyond planar geometries by constructing quantum dot arrays in three dimensions. We realize an eight-quantum dot system in a silicon-germanium heterostructure comprising two stacked germanium quantum wells, where quantum dots are positioned at the vertices of a cuboid. Using electrostatic gate control, we load a single hole into any of the eight quantum dots. To demonstrate the potential of multilayer quantum dot systems, we show coherent spin control and hopping-induced spin rotations by shuttling between the quantum wells. The ability to extend quantum dot arrays in three dimensions provides opportunities for novel quantum hardware and high-connectivity quantum circuits.

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arXiv2025

QARPET: A Crossbar Chip for Benchmarking Semiconductor Spin Qubits

Large-scale integration of semiconductor spin qubits into quantum processors hinges on the ability to characterize quantum components at scale, a task challenged by their operation at sub-kelvin temperatures, in the presence of magnetic fields, and by the use of radio-frequency signals. Here, we present QARPET (Qubit-Array Research Platform for Engineering and Testing), a scalable architecture for characterizing spin qubits using a quantum dot crossbar array. The crossbar features tightly-pitched spin qubit tiles and is implemented in planar germanium, by fabricating a large device with the potential to host 1058 hole spin qubits. Measurements on a patch of 40 tiles demonstrate key device functionality at millikelvin temperatures, including tile addressability, threshold voltage and charge noise statistics, and the characterisation of hole spin qubits and their coherence times in a single tile. These demonstrations pave the way for a new generation of quantum devices designed for the statistical characterisation of spin qubits.

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arXiv2024

Modular Autonomous Virtualization System for Two-Dimensional Semiconductor Quantum Dot Arrays

Arrays of gate-defined semiconductor quantum dots are among the leading candidates for building scalable quantum processors. High-fidelity initialization, control, and readout of spin qubit registers require exquisite and targeted control over key Hamiltonian parameters that define the electrostatic environment. However, due to the tight gate pitch, capacitive crosstalk between gates hinders independent tuning of chemical potentials and interdot couplings. While virtual gates offer a practical solution, determining all the required cross-capacitance matrices accurately and efficiently in large quantum dot registers is an open challenge. Here, we establish a modular automated virtualization system (MAViS) -- a general and modular framework for autonomously constructing a complete stack of multilayer virtual gates in real time. Our method employs machine learning techniques to rapidly extract features from two-dimensional charge stability diagrams. We then utilize computer vision and regression models to self-consistently determine all relative capacitive couplings necessary for virtualizing plunger and barrier gates in both low- and high-tunnel-coupling regimes. Using MAViS, we successfully demonstrate accurate virtualization of a dense two-dimensional array comprising ten quantum dots defined in a high-quality Ge/SiGe heterostructure. Our work offers an elegant and practical solution for the efficient control of large-scale semiconductor quantum dot systems.

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arXiv2023

A vertical gate-defined double quantum dot in a strained germanium double quantum well

Gate-defined quantum dots in silicon-germanium heterostructures have become a compelling platform for quantum computation and simulation. Thus far, developments have been limited to quantum dots defined in a single plane. Here, we propose to advance beyond planar systems by exploiting heterostructures with multiple quantum wells. We demonstrate the operation of a gate-defined vertical double quantum dot in a strained germanium double quantum well. In quantum transport measurements we observe stability diagrams corresponding to a double quantum dot system. We analyze the capacitive coupling to the nearby gates and find two quantum dots accumulated under the central plunger gate. We extract the position and estimated size, from which we conclude that the double quantum dots are vertically stacked in the two quantum wells. We discuss challenges and opportunities and outline potential applications in quantum computing and quantum simulation.

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arXiv2020

Effect of quantum Hall edge strips on valley splitting in silicon quantum wells

We determine the energy splitting of the conduction-band valleys in two-dimensional electrons confined to low-disorder Si quantum wells. We probe the valley splitting dependence on both perpendicular magnetic field $B$ and Hall density by performing activation energy measurements in the quantum Hall regime over a large range of filling factors. The mobility gap of the valley-split levels increases linearly with $B$ and is strikingly independent of Hall density. The data are consistent with a transport model in which valley splitting depends on the incremental changes in density $eB/h$ across quantum Hall edge strips, rather than the bulk density. Based on these results, we estimate that the valley splitting increases with density at a rate of 116 $μ$eV/10$^{11}$cm$^{-2}$, consistent with theoretical predictions for near-perfect quantum well top interfaces.

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