Electrically Tuneable Variability in Germanium Hole Spin Qubits
Edmondo Valvo, Michele Jakob, Patrick Del Vecchio, Maximilian Rimbach-Russ, Stefano Bosco
TL;DR
The paper tackles dot-to-dot variability in Ge hole-spin qubits caused by local electrostatics and strain by proposing in-plane squeezing of quantum-dot confinement to imprint a controlled anisotropy and enable on-demand $g$-tensor control. Using a Luttinger–Kohn–Bir–Pikus framework with both homogeneous and inhomogeneous strain and disorder, they compute the qubit $g$-tensor by projecting the magnetic-field Hamiltonian onto the ground-state manifold and analyze disorder ensembles. They find that squeezed QDs align the $g$-tensor along the long axis and significantly suppress both the magnitude and angular variability for selected in-plane magnetic fields, with unstrained Ge offering the strongest robustness; larger, smoother confinements further reduce variability. These results provide practical design guidelines for scalable, reproducible Ge-based spin-qubit processors, highlighting unstrained Ge squeezed dots as a particularly promising route for large-scale quantum computation.
Abstract
Hole spin qubits in planar germanium heterostructures are frontrunners for scalable semiconductor quantum computing. However, their current performance is mostly limited by large dot-to-dot variability that leads to uncontrolled qubit energies and random tilts in the spin quantization axis. Here, we propose a systematic and local method to engineer the spin qubit response by imprinting a controlled anisotropy in the quantum dot confinement, enabling on-demand electric g-tensor control. In particular, we find that both the quantum-dot size and asymmetry allow electrical tuning of the g-tensor and significantly suppress magnitude and angular variability of the spin response for selected magnetic field directions. We confirm this behavior by analyzing single-disorder realizations and statistical ensembles in state-of-the-art strained and unstrained germanium channels, showing that the latter provides an optimal path for $g$-tensor engineering. Our results provide practical design principles for on-demand control of the spin response and mitigating variability, paving the way towards large-scale germanium-based quantum computers.
