Despite the recent advancements by deep learning methods such as AlphaFold2, in silico protein structure prediction remains a challenging problem in biomedical research. With the rapid evolution of quantum computing, it is natural to ask whether quantum computers can offer some meaningful benefits for approaching this problem. Yet, identifying specific problem instances amenable to quantum advantage and estimating the quantum resources required are equally challenging tasks. Here, we share our perspective on how to create a framework for systematically selecting protein structure prediction problems that are amenable for quantum advantage, and estimate quantum resources for such problems on a utility-scale quantum computer. As a proof-of-concept, we validate our problem selection framework by accurately predicting the structure of a catalytic loop of the Zika Virus NS3 Helicase, on quantum hardware.

中文翻译：

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使用量子计算机预测蛋白质结构的视角

尽管 AlphaFold2 等深度学习方法最近取得了进展，但计算机模拟蛋白质结构预测仍然是生物医学研究中的一个具有挑战性的问题。随着量子计算的快速发展，人们很自然地会问量子计算机是否可以为解决这个问题提供一些有意义的好处。然而，识别适合量子优势的特定问题实例并估计所需的量子资源同样具有挑战性。在这里，我们分享了我们对如何创建一个框架来系统地选择适合量子优势的蛋白质结构预测问题的观点，并在公用事业规模的量子计算机上估计此类问题的量子资源。作为概念验证，我们通过在量子硬件上准确预测寨卡病毒 NS3 解旋酶催化环的结构来验证我们的问题选择框架。