Machine Learning-Assisted Power Modeling and Simulation of Quantum Cloud Data Centers
Bibliographic record
Abstract
Quantum Cloud Data Centers (QCDCs) are one of the emerging fields that considerably enhance data computing by integrating quantum processing capabilities with conventional cloud infrastructure, which leads to high computational power for tackling complex challenges in comparison to the classic Cloud Data Center (CDC). Although QCDCs hold paramount importance, a few researches have been developed directly focusing on them. To this end, this paper presents a Machine Learning (ML)-assisted modeling and simulation of a QCDC and its electrical power prediction. The system includes a Quantum Processing Unit (QPU), cryogenic dynamics, error correction, and power prediction. Key classes include Qubit for quantum bits with noise and crosstalk, Quantum Processor for multi-Qubit gates, Cryogenic System for thermal management, and Surface Code for error correction. The developed ML model estimates power using simulation data and Key Performance Indicators (KPIs). A central class unifies these, simulating circuits over 24,000 shots (24 hours) on a QCDC with two QPUs (200 Qubits each), testing a sample circuit with Hadamard, rotation, Control-Z (CZ), and measurement gates. Based on the conducted simulation results, the proposed model achieves KPIs well below 1.2, verifying its accuracy and performance in the predictive modeling of the proposed QCDC.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".