QFold: Quantum Walks and Deep Learning to Solve Protein Folding
This addresses protein folding for biochemical research, offering a scalable quantum approach with incremental improvements over prior quantum methods.
The authors tackled protein structure prediction by developing QFold, a hybrid quantum algorithm combining deep learning and quantum walks with a Metropolis algorithm, which avoids lattice simplifications and uses torsion angles for realism. They found a polynomial quantum advantage over classical analogs in tests and implemented a minimal version on IBMQ Casablanca.
Predicting the 3D structure of proteins is one of the most important problems in current biochemical research. In this article, we explain how to combine recent deep learning advances with the well known technique of quantum walks applied to a Metropolis algorithm. The result, QFold, is a fully scalable hybrid quantum algorithm that, in contrast to previous quantum approaches, does not require a lattice model simplification and instead relies on the much more realistic assumption of parameterization in terms of torsion angles of the amino acids. We compare it with its classical analog for different annealing schedules and find a polynomial quantum advantage, and implement a minimal realization of the quantum Metropolis in the IBMQ Casablanca quantum system.