Resource-Efficient Quantum Algorithm for Protein Folding
Predicting the three-dimensional (3D) structure of a protein from its primary
sequence of amino acids is known as the protein folding (PF) problem. Due to
the central role of proteins' 3D structures in chemistry, biology and medicine
applications (e.g., in drug discovery) this subject has been intensively
studied for over half a century. Although classical algorithms provide
practical solutions, sampling the conformation space 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 work, we present a model Hamiltonian with
$\mathcal{O}(N^4)$ scaling and a corresponding quantum variational algorithm
for the folding of a polymer chain with $N$ monomers on a tetrahedral lattice.
The model reflects many physico-chemical properties of the protein, reducing
the gap between coarse-grained representations and mere lattice models. We use
a robust and versatile optimisation scheme, bringing together variational
quantum algorithms specifically adapted to classical cost functions and
evolutionary strategies (genetic algorithms), to simulate the folding of the 10
amino acid Angiotensin peptide on 22 qubits. The same method is also
successfully applied to the study of the folding of a 7 amino acid neuropeptide
using 9 qubits on an IBM Q 20-qubit quantum computer. Bringing together recent
advances in building gate-based quantum computers with noise-tolerant hybrid
quantum-classical algorithms, this work paves the way towards accessible and
relevant scientific experiments on real quantum processors.
Anton Robert Panagiotis Kl. Barkoutsos Stefan Woerner Ivano Tavernelli