  Quantum computing for the biological sciences is an area of rapidly growing
interest, but specific industrial applications remain elusive. Quantum Markov
chain Monte Carlo has been proposed as a method for accelerating a broad class
of computational problems, including problems of pharmaceutical interest.
  Here we investigate the prospects of quantum advantage via this approach, by
applying it to the problem of modelling antibody structure, a crucial task in
drug development. To minimize the resources required while maintaining
pharmaceutical-level accuracy, we propose a specific encoding of molecular
dihedral angles into registers of qubits and a method for implementing, in
quantum superposition, a Markov chain Monte Carlo update step based on a
classical all-atom force field. We give the first detailed analysis of the
resources required to solve a problem of industrial size and relevance and find
that, though the time and space requirements of using a quantum computer in
this way are considerable, continued technological improvements could bring the
required resources within reach in the future.
