Teleport-Stabilized Quantum-Walk Transport for Robust Neoantigen Ranking in Near-Tie Regimes

Abstract Personalized neoantigen vaccination is a patient-specific decision problem: given a tumor’s molecular signature—somatic mutations, clonality, RNA expression, and antigen-processing context—we must choose a small, manufacturable peptide set that stays therapeutically relevant under uncertainty. In late-stage pipelines, candidates often collapse into near-ties: binding/presentation estimates, immunogenicity surrogates, and structure-based refinement compress many peptides into narrow score bands, making the final top-K fragile to small shifts in calibration, scaling, sampling, or docking protocols. Similar instability arises in peptide–target discovery when multiple hypotheses remain comparably supported. We introduce a transport-stabilized ranking layer that prioritizes redundancy structure over marginal score differences. Peptides (and structural microstates) become nodes in a patient-conditioned evidence graph; edges encode evidence overlap (motifs/HLA restrictions, processing features, target neighborhoods, pocket/contact fingerprints). We apply symmetry-aware quotient reduction of a normalized graph operator, collapsing near-symmetric neighborhoods into basin units while preserving effective shortlist couplings. Discriminative basin fingerprints are then extracted using coherent quantum-walk transport, ∣ψ(t)⟩ = e―iHt∣ψ(0)⟩, with visitation P(v,t) = ∣⟨v∣ψ(t)⟩∣2. Because coherent dynamics are oscillatory and horizon-dependent, we introduce a teleport-consensus channel that mixes unitary transport with restart to yield a stationary marginal suitable for stable ranking, ρt+1 = (1 ― α)UρtU† + α Σjvj∣j⟩⟨j∣, and πi = Tr(Πiρ). Information-theoretic polygraphs—entropy, dispersion, and consensus traces—quantify stabilization and provide an interpretable tie-breaking audit trail. We demonstrate consistent stabilization across colorectal-cancer contexts spanning peptide–target mechanistic triage, microstate symmetry auditing, multimodal evidence fusion, docking-ensemble geometrization, and patient-specific neoantigen shortlist construction. Competing Interest Statement The authors have declared that no competing interests exist.