Peptide Structure Prediction Using Counter-Diabatic Quantum Approximate Optimization Algorithm (CD-QAOA)
In this study, we predicted the structure of the heptapeptide APRLRFY, a neuropeptide sequence, on a tetrahedral lattice using a Quantum Approximate Optimization Algorithm (QAOA). QAOA is based on the adiabatic approximation and has been successfully applied to a wide range of optimization problems. However, relatively slow convergence during ground-state searches has frequently been reported. To overcome this limitation, we employed the Counter-Diabatic Quantum Approximate Optimization Algorithm (CD-QAOA), which introduces an additional counter-diabatic driving term into the adiabatic framework to accelerate convergence toward the ground state during peptide structure prediction. In the heptapeptide structure prediction, intermolecular interactions were modeled using two different approaches. In the first approach, only the interaction between the second residue, proline (P), and the seventh residue, tyrosine (Y), was included in the optimization. In the second approach, all residue-residue interactions within the heptapeptide were modeled using the Miyazawa-Jernigan (MJ) interaction matrix. To validate the peptide structures predicted using CD-QAOA, we additionally employed several classical computational methods, including quantum chemistry-based Hartree-Fock (HF) calculation and Density Functional Theory (DFT) calculation, conventional molecular dynamics (MD) simulation, and Hamiltonian replica exchange molecular dynamics (H-REMD) simulation. The structural similarities among the conformations obtained from these different approaches were systematically analyzed. CD-QAOA is highly effective for predicting the structures of short peptides. In particular, we demonstrate that a quantum-classical hybrid framework can significantly improve both the efficiency and accuracy of peptide structure prediction.