Towards Readout-Aware Layout Synthesis for Spin Qubit Systems with Double Quantum Dot Readouts
Bibliographic record
Abstract
A quantum compiler is required to run a quantum circuit on a quantum computer, similar to how classical compilers are required to run programs on classical computers. Spin qubits are a promising candidate for scalable quantum computers. General-purpose quantum compilers can be used for spin qubit devices, however they do not consider additional constraints imposed due to Double-Quantum-Dot (DQD) readout. For DQD readout, when a qubit in an algorithm is measured, the compiler must remap and re-route it to be adjacent to a special-purpose readout qubit, which is preserved in a known state. Moreover, this should be done in a way that minimizes overhead and maximizes the fidelity, in particular, due to noise in the readout process. This work formulates readout constraints to extend SMT (Satisfiability Modulo Theory)-based layout synthesis techniques proposed in [1] to spin qubit architectures. We define a metric, readout depth, to quantify the overheads incurred. Preliminary results, obtained from benchmarking GHZ and variational quantum eigensolver circuits on architectures with grid topologies, highlight the algorithmic tradeoffs that arise. We expect ongoing work on scaling up the methods to a larger set of algorithms will help inform the decisions of spin qubit hardware designers.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".