Tamavaq™: A Hybrid Quantum–Classical Grover Pipeline for Precision Neoantigen Vaccination in Glioma

Peptide vaccination for primary brain tumors is entering a pragmatic era, enabled by convergent advances in physics-aware artificial intelligence, structure/energetics modeling, and hybrid quantum search. We present Tamavaq™ , a closed-loop platform that fuses (i) multi-omics–driven sequence priors (NetMHC-family processing, TAP, expression; CNN embeddings), (ii) quantum-geometric encodings that supply a positive-semidefinite kernel and Fubini–Study geometry over candidate epitopes, and (iii) explicit structural/energetic evidence from PepSite-style projections and HPEPDOCK ensembles mapped to thermodynamic units via (\Delta G^\circ = RT \ln K_d) [70,97–101,176,184–186,200–201]. Within this stack, a Grover-style classical-in-the-loop oracle marks peptides that jointly satisfy immunogenicity, structural plausibility, and energetic sufficiency, allowing shallow amplitude amplification to concentrate wet-lab effort under realistic device noise [23–25,284–285]. By enforcing concordance across sequence, geometry, structure, and energy , and by expressing decisions in physical units interpretable at the bench and bedside, this platform provides a principled path to faster, auditable, and more durable patient-specific vaccines for glioma , with immediate extensibility to other solid tumors [1–6,23–25,70,97–101,176,184–186,192–194,200–201,271–277,284–289].