  We conduct an extensive study on using near-term quantum computers for a task
in the domain of computational biology. By constructing quantum models based on
parameterised quantum circuits we perform sequence classification on a task
relevant to the design of therapeutic proteins, and find competitive
performance with classical baselines of similar scale. To study the effect of
noise, we run some of the best-performing quantum models with favourable
resource requirements on emulators of state-of-the-art noisy quantum
processors. We then apply error mitigation methods to improve the signal. We
further execute these quantum models on the Quantinuum H1-1 trapped-ion quantum
processor and observe very close agreement with noiseless exact simulation.
Finally, we perform feature attribution methods and find that the quantum
models indeed identify sensible relationships, at least as well as the
classical baselines. This work constitutes the first proof-of-concept
application of near-term quantum computing to a task critical to the design of
therapeutic proteins, opening the route toward larger-scale applications in
this and related fields, in line with the hardware development roadmaps of
near-term quantum technologies.
