An Enhanced Hybrid Approach Using D-Wave's CQM to Solve the Phase Unwrapping Problem
Bibliographic record
Abstract
Quantum computers and algorithms are advancing rapidly, showing promise in tackling complex computational challenges. However, current quantum annealers are limited in handling large-scale problems due to their limited size and inherent noise. As a near-term solution, hybrid computing has emerged, harnessing the strengths of classical and quantum computing together. The phase unwrapping problem is a compu- tationally challenging task in interferometric synthetic aperture radar (In-SAR) image processing. In this study, we propose a hybrid approach to address the phase unwrapping problem for images larger than what current quantum annealers can handle. We utilized D-Wave's hybrid workflow, followed by refinements to enhance its performance. Our approach involves decomposing images and solving the resulting sub-images usingD- Wave's Constrained Quadratic Model (CQM), and then combining them. We assume that the obtained labels of sub-images are within an additive factor of the true labels. To determine these additive factors, we allow sub-images to overlap and use the labels of overlapping sectors for additive factor determination. Another phase of D- Wave's CQM is used for obtaining these shift factors. This enhanced CQM method results in improved outcomes compared to applying CQM to the entire image. Moreover, the proposed method enables unwrapping images larger than those that CQM can handle.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".