Empirical Validation and Break-Even Sensitivity Analysis of the QC-PEDA Cost-Model Partitioner
Abstract
Abstract The QC-PEDA architecture's central novelty is an explicit, weighted cost-model rule, path* = argmin_x [α·Latency(x,t) + β·Energy(x,t) − γ·Quality(x,t)], for routing analytics subtasks between classical and quantum execution. Despite this rule being the architecture's defining contribution, no prior study in the QC-PEDA evaluation program has implemented and tested it directly — all prior studies compared classical and quantum performance in isolation without exercising the partitioning rule itself. This paper closes that gap by building an 86-task empirical pool from the real Latency, Quality, and Energy-proxy data already collected across two prior QC-PEDA studies (noise-aware and multi-start evaluations), then applying the actual argmin partitioning rule across a grid of (α, β, γ) weight settings. At current empirical problem scale (n ≤ 12), classical execution strictly Pareto-dominates quantum execution on every measured task — lower latency, lower energy-proxy cost, and higher quality simultaneously — so no tested weight setting routes any task to the quantum path (0% quantum routing throughout). A break-even sensitivity analysis, hypothetically scaling classical execution latency to represent larger, post-exascale-representative classical workloads, finds that a Latency-dominant weight configuration begins routing to quantum once classical latency exceeds approximately 5 seconds (10,000 times the measured baseline), reaching 100% quantum routing by 50 seconds,…
Read the paper
Related studies
- Chaos and quantum thermalization
- The Dalton quantum chemistry program system
- Cord Blood Leptin and Adiponectin as Predictors of Adiposity in Children at 3 Years of Age: A Prospective Cohort Study
- Quantitative metabolomics by H-NMR and LC-MS/MS confirms altered metabolic pathways in diabetes
- Stable adiabatic quantum batteries
- A novel self-lipid antigen targets human T cells against CD1c(+) leukemias
Go deeper in Evidence Hub
Evidence Hub adds a study data sheet (design, sample size, evidence level, key numbers), an AI summary and a chat grounded in the paper's text.