Towards scalable cryogenic quantum dot biasing using memristor-based DC sources
Why this work is in the frame
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Bibliographic record
Abstract
Cryogenic memristor-based DC sources offer a promising avenue for in situ biasing of quantum dot arrays. In this study, we present experimental results and discuss the scaling potential for such DC sources. We first demonstrate the operation of a commercial discrete operational amplifier down to 1.2K which is used on the DC source prototype. Then, the tunability of the memristor-based DC source is validated by performing several 250mV-DC sweeps with a resolution of 10mV at room temperature and at 1.2K. Additionally, the DC source prototype exhibits a limited output drift of $\approx1\mathrm{μVs^{-1}}$ at 1.2K. This showcases the potential of memristor-based DC sources for quantum dot biasing. Limitations in power consumption and voltage resolution using discrete components highlight the need for a fully integrated and scalable complementary metal-oxide-semiconductor-based (CMOS-based) approach. To address this, we propose to monolithically co-integrate emerging non-volatile memories (eNVMs) and 65nm CMOS circuitry. Simulations reveal a reduction in power consumption, down to $\mathrm{10μW}$ per DC source and in footprint. This allows for the integration of up to one million eNVM-based DC sources at the 4.2K stage of a dilution fridge, paving the way for near term large-scale quantum computing applications.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it