  Simulating spontaneous structural rearrangements in macromolecules with
classical Molecular Dynamics (MD) is an outstanding challenge. Conventional
supercomputers can access time intervals up to tens of $\mu$s, while many key
events occur on exponentially longer time scales. Transition path sampling
techniques have the advantage of focusing the computational power on
barrier-crossing trajectories, but generating uncorrelated transition paths
that explore diverse conformational regions remains an unsolved problem. We
employ a path-sampling paradigm combining machine learning (ML) with quantum
computing (QC) to address this issue. We use ML on a classical computer to
perform a preliminary uncharted exploration of the conformational space. The
data set generated in this exploration is then post-processed to obtain a
network representation of the reactive kinetics.
  Quantum annealing machines can exploit quantum superposition to encode all
the transition pathways in this network in the initial quantum state and ensure
the generation of completely uncorrelated transition paths. In particular, we
resort to the DWAVE quantum computer to perform an all-atom simulation of a
protein conformational transition that occurs on the ms timescale. Our results
match those of a special purpose supercomputer designed to perform MD
simulations. These results highlight the role of biomolecular simulation as a
ground for applying, testing, and advancing quantum technologies.
