Spin Qubit Compact Model Enabling Co-Simulation of Quantum Dynamics with Classical Control Electronics
Bibliographic record
Abstract
Silicon quantum-dot (QD) spin qubits combine long coherence, foundry compatibility, and dense integration, but their performance ultimately depends on cryogenic CMOS control electronics whose non-ideal behavior feeds back onto the qubits. Predicting system-level fidelity therefore demands a joint quantum–classical simulation flow that mainstream SPICE tools do not yet provide.We introduce a compact model for silicon QD spin qubits designed for integration with standard CMOS electronic design automation (EDA) tools. This model enables co-simulation of qubits and CMOS control circuits, capturing key non-idealities such as T1and T2decoherence, as well as waveform distortion and quantization effects introduced by the CMOS digital-to-analog converter (DAC) generating the control pulses. Simulations of single- and two-qubit gate sequences, performed within a commercial SPICE simulator, reproduce the expected quantum dynamics with high fidelity, confirming the validity of the model and QD/CMOS co-simulation framework. By addressing a critical gap in hybrid quantum–classical co-design and co-simulation, this work provides a practical, EDA-ready framework for modeling, designing, optimizing, and scaling silicon-based quantum processors.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".