Findings: On the original quantum-network dataset, the best nonlinear model, Random Forest, reduces mean absolute error (MAE) by only 4.0% relative to Ridge regression with one-hot-encoded categorical features (2.500 versus 2.605), while Ridge reduces MAE by 49.5% relative to a constant predictor (5.160). Nearly all predictive signal is associated with memory family; every individual numerical feature has |r| < 0.08 with the target memory budget mc. In four of five controlled synthetic scenarios—threshold effects, saturation, phase transitions, and family-specific nonlinearity—the best tree ensemble reduces MAE by 31–66% relative to Ridge. In the heterogeneous scenario, Random Forest also reduces the observed under-dimensioning frequency from 55.5% to 35.0%. For a two-way numerical interaction, however, the improvement is only 12.5%. These single-split results support Ridge with one-hot encoding as a necessary baseline, but do not establish a universal model-selection threshold.
Method: The original dataset contains 1,000 seed-averaged entanglement-buffering configurations spanning six memory families, nine topology classes, three noise levels, seven traffic levels, and five network sizes (N ϵ {10,15,20,30,50}), with mc ϵ [1,20]. We compare a constant predictor, single-feature linear regression, multivariable linear regression, Ridge regression, Random Forest, and XGBoost on one fixed 60/20/20 train/validation/test split. MAE is the primary metric, supplemented by tolerance accuracy and the fraction of predictions below the target. To probe where nonlinear models begin to add value, we construct five synthetic datasets of 1,000 observations each with controlled threshold, interaction, saturation, phase-transition, and family-heterogeneous response functions. Because all reported comparisons use one split and seed, differences of a few percentage points are exploratory.
Keywords: Quantum Networks, Quantum Memories, Memory Allocation, Machine Learning, Regression Baselines, Tabular Data, Under-Provisioning
Citation: Vesterlund, D. (2026). When Is Machine Learning Needed for Quantum Memory Budget Prediction? A Baseline Study with Extreme-Scenario Boundary Exploration. Int J Math Expl & Comp Edu.3(3):1-6.