Optimal Service Placement for 6G Edge Computing With Quantum-Centric Optimization in Real Quantum Hardware
Bibliographic record
Abstract
Mobile edge computing (MEC) is emerging as a transformative enabler for the sixth-generation (6G) wireless networks. This letter investigates the potential of a quantum-centric optimisation approach for service placement in 6G MEC. Specifically, we formulate a mixed-integer binary programming problem that aims to minimise both service costs and delay by optimising service placement decisions, subject to constraints on service availability and the computing budget of edge servers. The formulated problem is NP-hard, making it computationally challenging for classical methods to solve. To address this, we develop a quantum-centric optimisation solution that efficiently finds optimal binary solutions for the problem, demonstrating potential for tackling medium-to-large-scale instances. Simulation results validate the effectiveness of the quantum-centric approach by showcasing the convergence pattern of the optimisation process on real quantum hardware and the associated increase in running time compared to a classical method.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.004 | 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".