Robust surface code topology against sparse fabrication defects in a superconducting-qubit array
Bibliographic record
Abstract
Superconducting qubits arrays have become one of the most promising architectures for the practical implementation of universal quantum computing with quantum error correction codes. Here we propose a defect-tolerant surface code topology which is resistant to sparse fabrication defects. The topology is equivalent to a disk folded into $N$ layers. A physically defective qubit can be replaced with a working physical qubit within the same unit cell from a different layer. Thereby sparse fabrication errors can be collected into one sacrificial layer and isolated from the working layers by turning off their controllable couplers. We propose two schemes to realize this robust topology. One is to use flux qubits, and the other is based on Xmon qubits. A modified quantum circuit for the stabilization cycle of the Xmon qubits scheme is developed. We calculate and compare the per-operation error thresholds for these two schemes and find that the error threshold for the modified quantum circuit is close to that for the original stabilization cycle. A possible way to implement the intersecting connections between different Xmon qubits on the circuit level is also provided.
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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) | 0.000 | 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".