Quantum Sampling Architecture for Protein Structure Reconstruction on Utility-Scale Hardware

2026-07-07

AI Summary

This work addresses the challenge of predicting short peptide conformations within protein binding pockets—a task traditionally hindered by computationally expensive physics-based conformational searches. The authors propose QSAD, a novel framework that formulates peptide structure prediction as a non-iterative quantum sampling problem over amino acid–level Hamiltonians. Implemented on IBM Heron R2 via a quantum–classical hybrid architecture, QSAD eliminates conventional iterative optimization and instead employs noise-tolerant, coarse-grained quantum sampling. This approach significantly enhances prediction accuracy and robustness while enabling approximate reconstruction of the protein energy landscape. Evaluated on 101 peptide complexes, QSAD outperforms current AI and quantum baselines by 27–71% in accuracy, achieves the lowest variance, tolerates hardware-level noise at 3–5 times typical error rates, and accelerates quantum execution by 27× compared to VQE.

Abstract

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.

Problem

• protein structure prediction
• conformational search
• short peptides
• binding pockets
• utility-scale hardware

Innovation

• quantum sampling
• protein structure prediction
• Hamiltonian evolution
• noise resilience
• quantum-classical hybrid