Quantum Sampling Architecture for Protein Structure Reconstruction on Utility-Scale Hardware
Provides a practical quantum-classical method for physics-based conformational search in peptide structure prediction, a regime where data-driven methods fail.
QSAD reformulates peptide structure prediction as Hamiltonian sampling on quantum hardware, achieving 27-71% accuracy improvement over AI and quantum baselines on 101 binding-pocket peptides, with 27x faster execution than VQE and tolerance to 3-5x higher noise.
Predicting the structure of short peptides in protein binding pockets remains difficult because this regime requires physics-based conformational search, yet existing methods do not provide a practical way to carry out that search on current hardware. We present QSAD, a quantum-classical framework that reformulates peptide structure prediction as amino-acid-level Hamiltonian sampling and replaces iterative optimization with non-iterative Hamiltonian evolution. Executed entirely on IBM Heron R2 across 101 binding-pocket peptides (5-18 residues), QSAD improves prediction accuracy by 27-71% over all evaluated AI and quantum baselines while maintaining the lowest variance across tested lengths. QSAD also tolerates noise levels 3-5x beyond typical hardware error rates, where iterative methods fail, and reduces mean quantum execution time by 27x relative to VQE. The sampled ensemble further supports approximate reconstruction of protein energy landscapes. These results establish coarse-grained quantum sampling as a practical computational path for structure prediction in regimes where data-driven methods lack sufficient signal.