  We develop quantum computational tools to predict how proteins fold in 3D,
one of the most important problems in current biochemical research. 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
validate a proof-of-concept realization of the quantum Metropolis in IBMQ
Casablanca quantum processor.
