Network architecture for a topological quantum computer in silicon
Bibliographic record
Abstract
Abstract A design for a large-scale surface code quantum processor based on a node/network approach is introduced for semiconductor quantum dot spin qubits. The minimal node contains only seven quantum dots, and nodes are separated on the micron scale, creating useful space for wiring interconnects and integration of conventional transistor circuits. Entanglement is distributed between neighbouring nodes by loading spin singlets locally and then shuttling one member of the pair through a linear array of empty dots. A node contains one data qubit, two ancilla qubits, and additional dots to facilitate electron shuttling and measurement of the ancillas. A four-node GHZ state is realized by sharing three internode singlets followed by local gate operations and ancilla measurements. Further local operations produce an X or Z stabilizer on the four data qubits, which is the fundamental operation of the surface code. Electron shuttling is simulated in the single-valley case using a simple gate electrode geometry without explicit barrier gates, and demonstrates that adiabatic transport is possible on timescales that do not present a speed bottleneck to the processor. An important shuttling error in a clean system is uncontrolled phase rotation of the spin due to modulation of the electronic g -factor during transport, owing to the Stark effect. This error can be reduced by appropriate electrostatic tuning of the stationary electron’s g -factor. While these simulations are unrealistic in neglecting spin–orbit, valley and decoherence effects, they are realistic with respect to the gate-induced potential landscape and are a first step towards more realistic modelling. Using reasonable noise models, we estimate error thresholds with respect to single and two-qubit gate fidelities as well as singlet dephasing errors during shuttling. A twirling protocol transforms the non-Pauli noise associated with exchange gate operations into Pauli noise, making it possible to use the Gottesman–Knill theorem to efficiently simulate large codes.
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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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".