Quantum Machine Learning for Controller Placement in Software Defined Networks
Bibliographic record
Abstract
Future 6G networks will be enabled by full softwarization of network functions & operations and in-network intelligence for self-management and orchestration. However, the intelligent management of a softwarized network will require massive data mining, analytics, and processing. That is why it is fundamental to find additional resources like quantum technologies to help achieve 6G key performance indicators. Quantum properties provide quantum computers to run a quantum algorithm with lesser queries. Quantum Machine Learning (QML) studies machine learning techniques on quantum computers. In this work, we use a QML algorithm to solve the controller placement problem for a multi-controller Software Defined Network (SDN). The network delay depends on where the controller is located, thus, it is critical to choose controllers at positions leading to minimize latency between the controllers and their associated switches. We consider an SDN architecture which is in its early stage of installation where the network nodes are deployed but connections will be established after obtaining controller locations, which results in the reduction of the overall controller to switch delay. By using different types of datasets, i.e., uniformly distributed and Gaussian distributed points, the experimental results show that the QML algorithm accelerates the SDN clustering methods (which are used to resolve the control placement problem) compared to those of the classical machine learning algorithm (like K-means) with comparable latency.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| 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".