What question did this study set out to answer?

To develop a hybrid optimization framework for accurate prediction of the native state of proteins.

February 23, 2026Open Access

Quantum-Classical Hybrid Optimization for Native State Protein Folding: A Sub-Femto Precision Approach via ARK5Q-200K

Key Points

To develop a hybrid optimization framework for accurate prediction of the native state of proteins.
Utilized ARK5Q-200K protocol combining quantum annealing and graph neural networks.
Mapped polypeptide torsional energy landscape onto a 200,000-qubit topological manifold.
Addressed Gibbs free energy minimization with high-precision.
Achieved sub-femto precision residual of 1.48 mHa in energy minimization.
Demonstrated capability to bypass the Levinthal paradox.
Established foundational baseline for synthesizing resilient biological tissues.

Abstract

Abstract This letter reports a hybrid quantum-classical optimization framework for predicting the native tertiary structure of proteins, specifically targeting a 20-amino acid sequence. Utilizing the ARK5Q-200K protocol, we integrate Quantum Annealing (QA) with Graph Neural Networks (GNN) to map the polypeptide torsional energy landscape onto a 200,000-qubit topological manifold. Our architecture resolves the Gibbs free energy minimization with a sub-femto precision residual of 1.48 mHa, effectively bypassing the Levinthal paradox through transcendental phase synchronization of the electronic cloud. By stabilizing the folding patterns against environmental decoherence, this study establishes a foundational baseline for the synthesis of high-resilience biological tissues. These results demonstrate that the CCristo infrastructure provides the necessary fidelity for advanced bio-adaptology in extreme orbital and interstellar environments.

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