Dynamic Qubit Routing with CNOT Circuit Synthesis for Quantum Compilation
Bibliographic record
Abstract
Many quantum computers have constraints regarding which two-qubit operations are locally allowed.To run a quantum circuit under those constraints, qubits need to be mapped to different quantum registers, and multi-qubit gates need to be routed accordingly.Recent developments have shown that compiling strategies based on Steiner tree provide a competitive tool to route CNOTs.However, these algorithms require the qubit map to be decided before routing.Moreover, the qubit map is fixed throughout the computation, i.e. the logical qubit will not be moved to a different physical qubit register.This is inefficient with respect to the CNOT count of the resulting circuit.In this paper, we propose the algorithm PermRowCol for routing CNOTs in a quantum circuit.It dynamically remaps logical qubits during the computation, and thus results in fewer output CNOTs than the algorithms Steiner-Gauss [14] and RowCol [27].Here we focus on circuits over CNOT only, but this method could be generalized to a routing and mapping strategy on Clifford+T circuits by slicing the quantum circuit into subcircuits composed of CNOTs and single-qubit gates.Additionally, PermRowCol can be used in place of Steiner-Gauss in the synthesis of phase polynomials as well as the extraction of quantum circuits from ZX-diagrams.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".