Abstract We develop quantum computational tools to predict the 3D structure of proteins, one of the most important problems in current biochemical research. We explain how to combine recent deep learning advances with the well known technique of quantum walks applied to a Metropolis algorithm. The result, QFold, is a fully scalable hybrid quantum algorithm that, in contrast to previous quantum approaches, does not require a lattice model simplification and instead relies on the much more realistic assumption of parameterization in terms of torsion angles of the amino acids. We compare it with its classical analog for different annealing schedules and find a polynomial quantum advantage, and implement a minimal realization of the quantum Metropolis in IBMQ Casablanca quantum system.

中文翻译：

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QFold：量子行走和深度学习来解决蛋白质折叠问题

摘要：我们开发了量子计算工具来预测蛋白质的 3D 结构，这是当前生化研究中最重要的问题之一。我们解释了如何将最近的深度学习进展与应用于 Metropolis 算法的众所周知的量子游走技术相结合。结果，QFold，是一种完全可扩展的混合量子算法，与以前的量子方法相比，它不需要晶格模型简化，而是依赖于更现实的关于氨基酸扭转角的参数化假设。我们将其与不同退火时间表的经典模拟进行比较，发现多项式量子优势，并在 IBMQ Casablanca 量子系统中实现量子 Metropolis 的最小实现。