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.