A Pre-Training Diagnostic for Amplitude-Encoding Failure in Quantum Classifiers, with Validated Symmetry-Breaking Remedies
Year: 2026
Type: preprint
Abstract: Simulator-only study of when amplitude-encoded variational quantum classifiers fail, with validated fixes. Real amplitude encoding obeys rho(x) = rho(-x). When preprocessing (typically z-scoring) drives class means antipodal, encoded classes collapse and a loss barrier appears. This work provides a one-pass pre-training risk score (class-mean cosine, with class-mean trace distance T and a linear probe) plus three A/B-tested symmetry-breaking remedies. Headline: class-mean cosine predicts classical-minus-QNN balanced-accuracy gap across 15 public binary datasets at Spearman rho = -0.72 (dataset-permutation p = 0.0034), with 10 datasets held out from calibration. Fixed binary cutoffs do not transfer as universal constants; the transferable instrument is the continuous score. Remedies recover selectively on flagged datasets (breast cancer 0.784 → 0.961, d_z up to 6.5). Synthetic sweep isolates mechanism (Spearman 1.0, classical at ceiling). All exact statevector, verified to ~1e-7. Contents of this upload (A1_arXiv_bundle.zip):- A1_manuscript.md, appendix theory, related work, references- figures/: 5 PNGs (300 dpi)- tables/: diagnostic tables, remedy A/B CSVs, correlation JSONs for Tables 1,2,5-9- code/: src/, run_experiment.py, key reproduce scripts, requirements.txt Reproduce headline: python scripts/balanced_accuracy_validation.pyFull test suite in source repo: python -m pytest (191 passed). Author: Muhammad Huzaifa Farooq, Independent Researcher. Contact: [email protected]. No specific funding. AI assistance (Claude Code) used for drafting/coding, all results verified by author.
Author: Farooq, Muhammad Huzaifa
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7208828825
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