论文标题
研究蛋白质晶格问题有限量子加速有限的潜力
Investigating the potential for a limited quantum speedup on protein lattice problems
论文作者
论文摘要
蛋白质折叠是计算生物学的核心挑战，在分子生物学，药物发现和催化剂设计中具有重要的应用。作为硬组合优化问题，已将其作为量子退火的潜在目标问题进行了研究。尽管文献中已经讨论了一些实验实现，但是这些方法的计算缩放尚未阐明。在本文中，我们介绍了应用于大量小肽折叠问题的量子退火的数值研究，旨在推断近期应用的有用见解。我们给出了两个结论：即使将天真的量子退火应用于蛋白质晶格折叠时，也有可能超越经典方法，并且对汉密尔顿人和涉及的时间表进行仔细的工程可以为此问题提供显着的相对改进。总体而言，我们的结果表明，量子算法可以很好地改善蛋白质折叠和结构预测领域的问题。
Protein folding is a central challenge in computational biology, with important applications in molecular biology, drug discovery and catalyst design. As a hard combinatorial optimisation problem, it has been studied as a potential target problem for quantum annealing. Although several experimental implementations have been discussed in the literature, the computational scaling of these approaches has not been elucidated. In this article, we present a numerical study of quantum annealing applied to a large number of small peptide folding problems, aiming to infer useful insights for near-term applications. We present two conclusions: that even naive quantum annealing, when applied to protein lattice folding, has the potential to outperform classical approaches, and that careful engineering of the Hamiltonians and schedules involved can deliver notable relative improvements for this problem. Overall, our results suggest that quantum algorithms may well offer improvements for problems in the protein folding and structure prediction realm.
