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Resource-efficient quantum algorithm for protein folding

机译：用于蛋白质折叠的资源有效量子算法

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Predicting the three-dimensional structure of a protein from its primary sequence of amino acids is known as the protein folding problem. Due to the central role of proteins' structures in chemistry, biology and medicine applications, this subject has been intensively studied for over half a century. Although classical algorithms provide practical solutions for the sampling of the conformation space of small proteins, they cannot tackle the intrinsic NP-hard complexity of the problem, even when reduced to the simplest Hydrophobic-Polar model. On the other hand, while fault-tolerant quantum computers are beyond reach for state-of-the-art quantum technologies, there is evidence that quantum algorithms can be successfully used in noisy state-of-the-art quantum computers to accelerate energy optimization in frustrated systems. In this work, we present a model Hamiltonian with $${mathcal{O}}({N}^{4})$$ scaling and a corresponding quantum variational algorithm for the folding of a polymer chain with N monomers on a lattice. The model reflects many physico-chemical properties of the protein, reducing the gap between coarse-grained representations and mere lattice models. In addition, we use a robust and versatile optimization scheme, bringing together variational quantum algorithms specifically adapted to classical cost functions and evolutionary strategies to simulate the folding of the 10 amino acid Angiotensin on 22 qubits. The same method is also successfully applied to the study of the folding of a 7 amino acid neuropeptide using 9 qubits on an IBM 20-qubit quantum computer. Bringing together recent advances in building gate-based quantum computers with noise-tolerant hybrid quantum-classical algorithms, this work paves the way towards accessible and relevant scientific experiments on real quantum processors.
机译：从其初级氨基酸序列中预测蛋白质的三维结构被称为蛋白质折叠问题。由于蛋白质结构在化学，生物学和药物应用中的核心作用，该主题已经集中研究了半个世纪。尽管经典算法为小蛋白质的构象空间采样提供了实际解决方案，但是即使在减少到最简单的疏水极模型时，它们也不能解决问题的内在NP硬复杂性。另一方面，虽然容错量子计算机超出了最先进的量子技术，但有证据表明量子算法可以成功地用于嘈杂的最先进的量子计算机以加速能量优化在沮丧的系统中。在这项工作中，我们展示了一个模型Hamiltonian，其中$$ { mathcal {4}）$$缩放和相应的量子变分算法，用于在格子上用n单体折叠聚合物链。该模型反映了蛋白质的许多物理化学性质，从而减少了粗粒颗粒的差距和仅仅是格子模型。此外，我们使用稳健和多功能的优化方案，使变分量子算法特异性地适应经典成本函数和进化策略，以模拟10个氨基酸血管紧张素的折叠在22夸脱。同样的方法也成功地应用于在IBM 20-Qubit量子计算机上使用9 Qubits的7个氨基酸神经肽的折叠研究。近期建设基于栅极的量子计算机的最新进展，具有耐噪声的混合量子古典算法，这项工作为真正的量子处理器提供了可访问和相关的科学实验。

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