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.

翻译：我们针对近期的量子计算机在计算生物学领域的一项任务进行了广泛研究。通过构建基于参数化量子电路的量子模型，我们对与治疗性蛋白质设计相关的序列分类任务进行了序列分类，并发现其与同规模经典基线模型相比具有竞争性表现。为研究噪声影响，我们在最先进噪声量子处理器的模拟器上运行了部分资源需求最优的量子模型，并应用误差缓解方法以改善信号。随后，我们在Quantinuum H1-1离子阱量子处理器上执行这些量子模型，观察到其与无噪声精确模拟的结果高度一致。最后，我们采用特征归因方法发现，量子模型至少能与经典基线模型一样识别出合理的关系。这项工作构成了近期量子计算在治疗性蛋白质设计关键任务中的首次概念验证应用，为根据近期量子技术硬件发展路线图，在该领域及相关领域开展更大规模应用开辟了道路。