Graph-Based Identification of Qubit Network (GidNET) for Qubit Reuse
Bibliographic record
Abstract
Quantum computing introduces the challenge of optimizing quantum resources crucial for executing algorithms within the limited qubit availability of current quantum architectures. Existing qubit reuse algorithms face a trade-off between optimality and scalability, with some achieving optimal reuse but limited scalability due to computational complexities, while others exhibit reduced runtime at the expense of optimality. This paper introduces GidNET (Graph-based Identification of qubit NETwork), an algorithm for optimizing qubit reuse in quantum circuits. By analyzing the circuit's Directed Acyclic Graph (DAG) representation and its corresponding candidate matrix, GidNET identifies higher-quality pathways for qubit reuse more efficiently. Through a comparative study with established algorithms, notably QNET [1], GidNET not only achieves a consistent reduction in compiled circuit widths by a geometric mean of 4.4%, reaching up to 21% in larger circuits, but also demonstrates enhanced computational speed and scaling, with average execution time reduction of 97.4% (i.e., 38.5× geometric mean speedup) and up to 99.3% (142.9× speedup) across various circuit sizes. Furthermore, GidNET consistently outperforms Qiskit in circuit width reduction, achieving an average improvement of 59.3%, with maximum reductions of up to 72% in the largest tested circuits. These results demonstrate GidNET's ability to improve circuit width and runtime, offering a solution for quantum computers with limited numbers of qubits.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".