Input-Data Information Preservation and Protocol-Specific Error Mitigation in Noisy Fixed-Parameter Quantum Front Ends: A Reproducible Simulation Study

Abstract

I examine whether a noisy fixed-parameter quantum front end preserves class information from input data before variational optimization. Only the input vector <inline-formula> <tex-math notation="LaTeX">$x$ </tex-math></inline-formula> is encoded; the label is used after circuit execution to estimate training distributions and score disjoint held-out inputs. The main density-matrix grid contains 10,800 directly simulated configurations over two binary tasks, widths <inline-formula> <tex-math notation="LaTeX">$n\in \{2,3,4\}$ </tex-math></inline-formula>, every integer depth <inline-formula> <tex-math notation="LaTeX">$L=1,\ldots,12$ </tex-math></inline-formula>, five depolarizing strengths, and 30 circuit seeds. At <inline-formula> <tex-math notation="LaTeX">$n=4$ </tex-math></inline-formula>, mean held-out mutual information has a finite-depth maximum that shifts from <inline-formula> <tex-math notation="LaTeX">$L=6$ </tex-math></inline-formula> to 5 for Gaussian data and from <inline-formula> <tex-math notation="LaTeX">$L=7$ </tex-math></inline-formula> to 6 for the Wisconsin Diagnostic Breast Cancer (WDBC) benchmark as <inline-formula> <tex-math notation="LaTeX">$p$ </tex-math></inline-formula> increases from 0 to 0.04. In five-fold complete-seed validation, including training-distribution mutual information with declared design variables raises held-out-accuracy <inline-formula> <tex-math notation="LaTeX">$R^{2}$ </tex-math></inline-formula> from 0.724 to 0.961 for Gaussian data and from 0.714 to 0.860 for WDBC. Training total variation is the stronger binary comparator in complete-seed validation; test-set total variation is reported only as a non-deployable post-outcome oracle. Paired seed-level RMSE improvements from mutual information are 0.0185 (95% bootstrap interval 0.0179–0.0190) and 0.00634 (0.00592–0.00675), and finite-shot training estimates retain most of that gain. Twenty independently seeded Gaussian realizations, twenty repeated WDBC partitions, per-input 256-shot tests, and explicit rotation-schedule ablations expose both transfer and limitations. A family-matched damped-alignment response outperforms an equal-parameter-count polynomial comparator under identical grouped folds but transfers weakly to some widths, encodings, and schedules. Explicit local-folding zero-noise extrapolation and tensor-product measurement-error mitigation improve information and held-out likelihood accuracy in selected simulated budgets; small-cluster sensitivity and failure cases are retained. The accompanying reproducibility archive contains the complete source, raw counts, parameter registries, 2,000-replicate bootstrap vectors, mitigation replicates, automated tests, and resumable one-click workflow. I make no hardware, trained-VQNN, clinical, quantum-advantage, or universal-scaling claim.