Designing a resilient cloud network fulfilled by quantum machine learning
Bibliographic record
Abstract
Next-generation digital services require resilient, energy-conscious cloud networks, but current optimization techniques are unable to quickly reconfigure infrastructures when a failure occurs. To address real-time service migration, this paper presents a quantum machine learning (QML) architecture that concurrently maximize quality of service (QoS) and minimizes migration cost while taking capacity, energy, and jitter restrictions into account. Thousands of migration methods are evaluated in parallel using a parameterized quantum neural network to solve the model. In comparison to the genetic algorithm, the QML optimizer reduces peak CPU load by 45%, while maintaining contractual QoS during cyberattacks, according to an experiment conducted on a real case study. The quantum solution offers noticeably smoother resource use, according to several assessments. These results establish QML as a promising facilitator for responsive cloud resilience by proving that quantum search may unleash fault-tolerant reconfiguration that is not possible with classical methodologies. The deployment is limited to medium-sized networks due to the size and noise of current quantum hardware; however, implementing new error-mitigation strategies provides viable routes to commercial use. This study establishes a research agenda for scalable quantum optimization in resilient networks in digital infrastructures by combining quantum computing with cloud-network engineering.
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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 | 0.000 |
| Science and technology studies | 0.001 | 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".