Memristor-Based Cryogenic Programmable DC Sources for Scalable In Situ Quantum-Dot Control
Bibliographic record
Abstract
Current quantum systems based on spin qubits are controlled by classical electronics located outside the cryostat. This approach creates a major wiring bottleneck, which is one of the main roadblocks toward scalable quantum computers. Thus, we propose a scalable memristor-based programmable dc source that can perform biasing of quantum dots (QDs) inside the cryostat. This novel cryogenic approach would enable to control the applied voltage on the electrostatic gates by programming the resistance of the memristors, thus storing in the latter the appropriate conditions to form the QDs. In this study, we first demonstrate multilevel resistance programming of TiO2 memristors at 4.2 K, an essential feature to achieve voltage tunability of the memristor-based dc source. We then report hardware-based simulations of the electrical performance of the proposed dc source. A cryogenic TiO2 memristor model fit on our experimental data at 4.2 K was used to show a 1 V voltage range and 100$\mu \text{V}$resolution in situ memristor-based dc source. Finally, we simulate the biasing of double QDs (DQDs), enabling 120 s stability diagrams. This demonstration is a first step toward advanced cryogenic applications for resistive memories, such as cryogenic control electronics for quantum computers.
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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".