Surface acoustic wave-induced strain engineering for quantum dot control in a non-piezoelectric material
Bibliographic record
Abstract
In this paper, we explore alternative approaches to integrate acoustic wave functionalities into non-piezoelectric group IV materials, notably, germanium within germanium-on-silicon (GoS) heterostructures. This material system is promising as a spin qubit platform for scalable quantum computing architectures. We investigate the potential of surface acoustic waves (SAWs) inducing strain as an interaction mechanism that can be used for the manipulation of spin quantum states in lateral gated quantum dot (QD) devices. Using the Bir–Pikus formalism and k · p theory, we describe the theoretical basis for strain-induced modulation of the valence band in the GoS heterostructures. Our simulations demonstrate that the Rayleigh-type SAWs operating at GHz frequencies can create strain profiles that effectively modulate energy levels in quantum dots. Furthermore, replacing the piezoelectric material (AlN in this work) with a non-piezoelectric counterpart (Al2O3) preserves high-quality acoustic wave propagation while enabling purely mechanical coupling mechanisms, thus enhancing qubit coherence by reducing charge noise, which in piezoelectric materials stems from direct coupling between charge fluctuations and phonons. Proxy simulations with a double-plunger-gate system and floating potentials confirm the sensitivity of QD charge configuration energies to induced strain. These findings support SAW-driven detuning and align with recent predictions on strain-mediated spin–orbit coupling in hole spin qubits. This study highlights the promise of SAW-based strain engineering for scalable quantum control in CMOS-compatible platforms, paving the way for future experimental validation and integration with phononic crystals to advance mechanically enabled quantum architectures.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".