Resource-Efficient Quantum Algorithm for Protein Folding

Due to the central role of proteins’ 3D structures in chemistry, biology and medicine applications (e.g., in drug discovery), protein folding has been intensively studied for over half a century. Although classical algorithms provide practical solutions for the conformation space sampling of small proteins, they cannot tackle the intrinsic NP-hard complexity of the problem, even reduced to its simplest Hydrophobic-Polar model.
While fault-tolerant quantum computers are still beyond reach for state-of-the-art quantum technologies, there is evidence that quantum algorithms can be successfully used on Noisy Intermediate-Scale Quantum (NISQ) computers to accelerate energy optimization in frustrated systems. In this talk, I will present a folding algorithm that requires resources (number of qubits and gate operations) that scale polynomially with the number of amino acids (AA). In particular, we propose a robust and versatile optimisation scheme to simulate the folding of the 10 AA Angiotensin peptide on 22 qubits and a 7 AA neuropeptide model using 9 qubits on an IBM Q 20-qubit quantum computer.